Download Chapter 3. The Money Demand Function

Chapter 3. The Money Demand Function
3.1 Introduction
In the previous chapter, we used a regression method to examine the
quantitative importance of the potential macroeconomic variables in the conduct of the
monetary policies in each country. In other words, we were concerned with the objectives
of the monetary policies in these countries. In this chapter, we investigate the function of
vital importance to actual implementation of monetary policy in any economy; the money
demand function.
The interest rate elasticity of the money demand is decisively critical, however
the stability of the money demand function is one of the most important recurring
themes in the theory and actual implementation of monetary policy. An existence of the
highly predictable money demand function with relatively few variables is one of the
necessary conditions for monetary policy to exert a significant effects upon real economy.1
Thus, in this chapter, we will closely analyze the stability properties of the money
demand function.
The structure of the chapter is as follows: in the next section, the empirical
specification of the money demand function as well as data selection will be briefly
reviewed. In Section 3, estimated results will be shown. The results of the recursive
Chow tests will be given in Section 4. The results of the several specification tests for the
money demand function will be discussed in Section 5. Other specifications of the money
demand function for the Philippines will be discussed in Section 6. Concluding remarks
are included in the final section.
3.2 The Model
It is well-known that several theoretical models of money demand such as
Keynes’s liquidity preference model or Tobin’s portfolio model should be regarded as a
“long-run” money demand function. Thus if those models are fitted directly to the
macroeconomic data, their statistical performance are not satisfactory in general.
However, adding of the lagged value of money supply as independent variable
improves statistical performance significantly. Two popular “rationalization” to justify
inclusion of lagged value are partial adjustment and adaptive expectation hypotheses.
1
The research effort by the staff of central banks on the existence of reliably stable
money demand function may simply signify this point.
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Though the relationship between these two hypotheses will be examined in detail later,
we simply start with the following conventional partial adjustment argument.
First, we posit that individuals have a desired or “long-run” demand for money
function of a conventional form. For this purpose, following function is proposed,
(3.1)
M ∗t = α + β 0 Yt + β1 rt
where M ∗t is the logarithm of the desired real balance public wants to hold, given their
real income and the interest rate. Furthermore let us assume following quadratic loss
function, C
(3.2)
C t = a ( M t − M *t ) 2 + b ( M t − M t −1 ) 2
First term in (3.2) represents costs of being out of long run equilibrium. This may include
costs in terms of interest income foregone, inability to make a purchase of goods, etc. It
must be noted that this cost is assumed to be symmetrical around equilibrium position.
Second term signifies costs of adjustment in money balances. This may encompass costs
in terms of inconvenience and time and so on. We assume that individuals choose
short-run money balance to minimize this quadratic loss function. Minimizing C in (3.2),
given the value of M ∗t , results in,
(3.3)
Mt =
b
a
M t −1 = γ M *t + (1 − γ ) M t −1
M *t +
a+b
a+b
That is, short-run desired money demand is a weighted average of the long-run money
demand and lagged value of money balance. Equation (3.3) can be written as famous
formulae,
(3.4)
M t − M t −1 = γ ( M *t − M t −1 )
represents so called the speed of the adjustment. It is worth noting that, though
equation (3.4) is a first order partial adjustment mechanism, inclusion of higher order of
cost term such as ( ∆M t − ∆M t −1 ) 2 results in higher order partial adjustment mechanism.
In any event substituting (3.4) into (3. 1) yields,
(3.5)
M t = γ α + β 0γ Yt + β1γ rt + (1 − γ ) M t −1
Hence we estimate the following Goldfeld’s type of money demand function for each
country,2
2
For more on these and related issues, see, for example, Laidler (1993, chapter 9) and
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(3.6)
M t = δ1 + δ 2 Yt + δ 3 rt + δ 4 M t −1
The short-run effects are measured by the composite coefficients, δ2 and δ3. Since we have
exact identification, the corresponding long-run coefficients are simply obtained by
dividing each of those by the estimate of coefficient of adjustment, given by the coefficient
of the lagged dependent variable.3 Though this type of “theory” based specification of
money demand function has been criticized on the ground that the lag structure of the
model is unduly constrained at the outset of the empirical investigation, we rather stick
to “old-fashioned” modelling strategy in this chapter.4
The data used in this chapter is from the same data source, and the variables
are seasonally adjusted as in the previous chapter. In the estimation of the money
demand function, first issue to consider is the choice of the money; it is well known that
there are momentous arguments to date, relating appropriate empirical counterparts of
money. M1 is the most basic definition of money and most of the empirical studies of
money demand function in the U.S. focus on this concept. However, in line with the
analyses of the former chapter, we adopt M2 component of the money for this chapter as
well.
Though the income variable does not present a serious problem, the selection of
the interest rate variable is, nonetheless, cumbersome; for the economies with rather
underdeveloped monetary markets, especially in the early stages of development, the
choice of the relevant alternative yields in the portfolio choice may not be trivial. For
example, some authors stressed the expected inflation rates, instead of the interest rate,
as an influential factor in the determination of the money demand for the developing
countries, since the relatively underdeveloped alternative financial markets make the
Cuthbertson (1985, chapter 4).
3
Alternatively, the coefficient of the lagged dependent variable can be interpreted as the
coefficient of the adjustment in an adaptive expectations. For a derivation of the reduced
form of the money demand function with partial adjustment and adaptive expectations,
see Cuthbertson (1985, chapter 4).
4
Alternative modelling strategy is well known “general to specific” strategy. See, for
example, Hendry, Pagan, and Sargan (1984), for an overview of this approach and related
topics. In Appendix 1 on the analyses of the error correction model, this alternative
modelling strategy is, in spirit, adopted.
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substitution between money and real assets rather more significant than that of between
money and financial assets.5 Furthermore, substitution between domestic money and
foreign currency or foreign bonds may be particularly relevant for some countries. 6
Another important factor of the determination of money demand for the developing
countries could be the existence of an unorganized financial market. Although there
might be some linkage between organized market and unorganized market, the returns
of the unorganized market are claimed as being much higher and more volatile than
those of the organized market. Though information of financial yields in unorganized
financial sector is hard to obtain, the movements of those returns could significantly
affect the private money demand in a developing country. 7 Finally the empirical
analyses of the money demand function for the developing countries may require careful
treatment of the long-run institutional changes. These factors may include factors such
as the ratio of the labor force outside the agriculture, the ratio of population to bank
offices, the ratio of currency to the total money stock and so on.8 With these reservations
in mind, we restricted ourselves to using money market rate, if available, as relevant
interest rates in the estimation of the money demand function.
In any event, for Japan, we used interbank-market rates as the typical interest
rate. We estimated separate money demand function for the fixed exchange rate period
and the flexible exchange rate period.
5
For example, Aghevli-Khan-Narvekar-Short (1979) used the expected inflation rate,
instead of interest rate in their analyses of the money demand function for selected Asian
countries, including Indonesia and the Philippines. Furthermore, Kim-Hur-Kim (1990)
used both nominal interest rate and the expected inflation rate in their analyses of
velocity of money for the Korea economy.
6
Another potential problem using interest rate in the money demand function for
developing counties in general is that interest rates for financial assets in developing
countries are often subject to tight control by monetary authorities. Furthermore, since
these administered interest rates have experienced only infrequent changes, it might be
difficult to obtain quantitative relationships between money and interest rate.
7
For the discussion of the specification issues of the money demand function for the
developing countries, see, for example, Adekunle (1978).
8
For these issues, see, for example, Bordo and Jonung (1987).
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For Korea, our problem was that as the money market rate was available only
from 1977, so in turn we adopted the yields of government bond as the representative
interest rate.9 The division of the sample period follows that of the previous chapter.
For Indonesia and the Philippines, the money market rates were available for
the entire sample period and the sample periods remain unchanged.
3.3 Estimated Money Demand Functions
In this section, we will review the results of money demand functions.
Estimated results are summarized in Table 3.1. Let us examine the results for individual
countries, respectively.
(i) Japan10
For the first period, the interest rate as well as the real GNP were significant
only at the 10% level. However, both real GNP and interest rate became significant at the
1% level for the flexible exchange rate regime. In terms of the long-run income
elasticities, it is surprising to see that long-run income elasticity for the second period
apparently exceeds one. This result might be explained by the whole range of new
financial instruments with rather market-determined interest rates.
The estimated result for the fixed exchange rate regime is not satisfactory with
usual standards. However, as done in Furukawa (1985), if we change the sample period
ending in 1974:IV, real GNP and interest rate variables are found to be significant at the
1% level.11
It is clear from this result that there existed a substantial structural break of the
9
We may note, however, that in the analysis of Korean money multiplier Keun (1983)
employed a weighted average of government and public bond rates, corporate bond rates,
and the time deposit rates.
10
For the results of the conventional money demand function for the early years of the
Japanese economy, see Hamada and Hayashi (1985), Tsutsui and Hatanaka (1982),
Koumura (1985), and Furukawa (1985). For recent contributions for conventional type of
money demand function, see , for example, Shiba (1991).
11
Furukawa (1985) examined the stability of the money demand function for the
Japanese economy from 1965:I to 1984:II. He used subsample periods, 1965:I to 1974:IV
and 1975:I to 1984:II to apply Chow test.
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money demand function across the sample periods. Moreover, the coefficients of the
interest rate for the flexible exchange rate period were much smaller from those of the
first period. Especially the long-run interest rate elasticity became half of that for the
first period. Since financial deregulation induces availability of money substitutes paying
competitive rates of return, it can be argued that the financial liberalization and the
financial internationalization were consistent with this finding.12
(ii) Korea
For the first period, though the real GNP was found to be statistically
significant at the 1% level, the interest rate was significant only at the 10% level. The
qualitative conclusions did hold for the second period as well: while real GNP turned out
to be significant at the 5% level, interest rate was only significant at the 10% level. In
terms of the long-run income elasticities, both estimates seem to be close enough to one.13
As in the case of Japan, it seems clear that there existed serious structural
change of the money demand function for the Korean economy as well. The coefficients of
the interest rate of the money demand function were considerably reduced in the second
period. Especially in terms of the long-run interest rate elasticities, estimate for the
second period is surprisingly lower than that of the first period. While the extent of the
financial liberalization for the Korean financial markets were often regarded to be less
radical, the institutional changes of the financial markets may explain these results.
(iii) Indonesia
For the first period, though interest rate is only significant at the 10% level,
real GNP turned out to be significant at the 1% level. Nevertheless, for the second
period, both variables remain significant only at the 20% level. In terms of the long-run
income elasticities, estimates for the two periods are extremely high (2.52 and 2.83,
respectively). Since these long-run income elasticities were calculated using the
coefficient of adjustment, implausibly long adjustment lags implied here could lead this
finding. However, these figures are not much different from the figures for Japan and
12
See, Roley (1985) for empirical evidences on this.
13
Kim-Hur-Kim (1990) also examined the income velocity of money and its stability for
the Korean economy from 1975:I to 1988:IV.
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Korea and comparable to the figures for the post-war U.S. results.14 Aghevli etc. (1978)
note that long-run income elasticities for the developing countries sometimes become
greater than one.15 As they argue, monetarization,16 limited scope of economization of
cash balance, and the scarcity of other financial instruments may explain these results.17
On the other hand, we may note that in the annual analyses of the money demand
function such as Meltzer (1963, U.S. 1900-1958), Lucas (1987, U.S. 1900-1985), Laidler
(1966, U.K. 1919-1960), Artis and Lewis (1984, U.K. 1920-1981), most of long run income
elasticities were close to unity.18
Somewhat surprisingly, the coefficients of the interest rate for two sample
period did not differ substantially from that of the first period. However, in terms of the
long-run elasticities, the figure for the second period is smaller than that of the first
period. This makes rather a sharp contrast with the cases of Japan and Korea. Finally
14
See, for example, Table 1 in Judd and Scadding (1982).
15
For example, in Balino (1978), the estimated long-run income elasticity for Argentina
for the period from 1935 to 1969 is 1.643.
16
For an detailed analysis on the concept and measurement of monetarization for the
developing countries, see Chandavarker (1977). See Bordo and Jonung (1987) for a
general discussion of the effects and measurements of monetarization for the money
demand function as well.
17
Aghevli etc. (1978) estimated the money demand function for Sri Lanka, Indonesia,
Malaysia, Philippines, Singapore, and Thailand using interpolation method to create
quarterly figure for the real GNP. The sample period for Philippines and Indonesia are
from 1957:II to 1977:IV, and from 1968:II to 1976:IV, respectively. Coincidently, their
study is thus rather complementary. Corresponding long-run income elasticities are
1.537 and 1.84, respectively. Hence long-run income elasticities for these counties
apparently became quite large in recent years. Whether this is the result of the swift
monetarization remains to be seen.
18
See also Fair (1987) for the international comparison of the estimates of the money
demand function for post-war period. In this context, it is rather interesting to know that
Toyota (1992) reports that the long-run income elasticities of the money demand function
of Japan for the periods, 1900-1944 and 1947-1988 are 1.186 and 1.101, respectively.
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TABLE 3.1: MONEY DEMAND FUNCTION
COUNTRY
PERIOD
JAPAN
CONSTANT
GNP
INT
M/P(-1)
LONG-RUN
LONG-RUN
COEFFICIENT OF
INCOME
INTEREST RATE
ADJUSTMENT
DW
1.02
.263
.118
.998
2.484
1.55
.131
.13
.998
1.551
1.23
.707
.276
.963
2.283
1.11
.134
.231
.996
1.286
2.52
.298
.161
.986
1.723
2.83
.202
.282
.982
2.248
1.96
*
.151
.851
2.639
1962:II - 1973:I
JAPAN
.01
.12
-.031
.882
(.082)
(1.639)
(-1.78)
(13.006)
1973:III - 1990:I
KOREA
-.895
.202
-.017
.87
(-3.41)
(3.438)
(-3.784)
(21.812)
.068
.339
-.195
.724
(.181)
(2.818)
(-1.778)
(5.959)
-.076
.257
-.031
.769
(-.380)
(2.291)
(-1.674)
(7.545)
-1.865
.405
-.048
.839
(-3.18)
(3.059)
(-2.022)
(12.273)
-5.012
.797
-.057
.718
(-2.138)
(2.253)
(-2.188)
(5.745)
.328
.296
-.007
.849
(.382)
(1.729)
(-.239)
(10.193)
1973:IV - 1979:IV
KOREA
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R2
1980:II - 1990:I
INDONESIA
INDONESIA
PHILIPPINE
1977:II - 1983:IV
1983:II - 1990:I
1981:II - 1990:I
Notes: GNP=real GNP, INT=interest rate, M/P(-1)=lagged value of real money balance.
R2=adjusted R2, DW=Durbin-Watson statistics.
Numbers in the parentheses indicate t values.
we may note that the coefficient on the lagged dependent variable substantially varies,
suggesting some change in the nature of adjustment process.
Since we had a data discontinuity problem for Indonesia, a serious reservation
of this results is in order. However, as substantial measures for the financial
liberalization were documented in Indonesia since 1983; this result poses an open
question.
(iv) The Philippines
Only the yields for the treasury bills were available for the market interest rate
variable. Though the real income was significant at the 10% level, the yields of TB were
not significant at all in this result. The long-run income elasticity is 1.98, far greater
than one. Thus Philippine financial economy might remain at the stage of the developing
economies. Further investigation of the money demand function will be discussed in
Section 3.5.
A concluding observation for foregoing empirical results seems to be as follows:
except in the Philippine case, the interest sensitivities of money demand for the
respective countries were not negligible, though not completely satisfactory, hence the
traditional monetary transmission mechanism for the monetary policy should deserve at
least some credit. Moreover, though instability of the money demand function will be
examined in the following sections, the problem seems to be the most apparent for Japan
and Korea at this stage.
3.4 Stability of the Money Demand Function
In this section, we will scrutinize in detail the stabilities of the money demand
function within a given sample period. Since the Philippine results for the money
demand function are not convincing yet, we will concentrate on other the countries.
As reviewed in Chapter 1, these countries examined here undertook serious
financial liberalization efforts in the 1980’s. Though financial liberalization, in general,
improves the efficiency of the financial sector, drastic change of the institutional setting
and the emergence of new financial instruments may create serious problems for the
stability of money demand function. For example, liberalization of interest rates,
inducing a reassessment of portfolios, may result in a discrete change in the interest
elasticity of money demand. Furthermore, the returns on the newly created financial
instruments could be significant determinants of the money demand. However, such
measures to improve the function of financial institutions may attract savings from the
unofficial market, as happened in Korea. If that is the case, it is possible that total
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money demand will increase.
Since Goldfeld’s (1976) well-known case of missing money, the stability of money
demand function became a cornerstone in applied macroeconomics. Vast empirical
studies examined this issue for various countries and time periods. Instability issue of
the money demand function were frequently related to regulatory changes, as mentioned
above. Hence, the stability of the money demand function deserves close examination
under these circumstances.19 For structural stability of the money demand function, see
also Judd and Scadding (1982) for different perspectives.
We used historical facts, i.e. the exchange rate system, to divide entire sample
years into two periods for Japan and Korea. Furthermore, the data discontinuity problem
with same institutional factor forced us to divide sample years into two periods for
Indonesia. Nonetheless, from the practical viewpoint, the stability of the money demand
function even within a given sample period can not be easily dismissed. One way to
pursue this issue is to use both historical facts and a statistical test such as the Chow
test to detect a single break point within a sample period. Nevertheless, even within a
given sample period, it is quite possible to have multiple break points. Using the usual
Chow test under those circumstances might lead to misleading conclusions, in general.
Hence, in the next subsection, the stability test with sequence of Chow test is reported to
detect possible break points. In this context, it must be emphasized that the regulatory
changes and financial innovations focused here were rather continuous; for example, in
Indonesia, interest rates and credit ceilings were deregulated in 1983, money market
instruments were introduced in 1984, the ceiling on swaps were removed in 1986, the
bank entry were allowed in 1988 and so on. Given those series of events which might
affect the behavior of the money demand function respectively, the importance of the use
of sequential stability test statistics would be reasonably founded.
3.4.1 Sequential Chow Test
Let us review the results of the test statistics for each country.
(i) Japan
The graph of the calculated test statistics for the first period is presented in
Figure 3.1. Money demand function became rather unstable in the period from 1969 to
19
See Lucas (1988) for a theoretical discussion of the stability of the money demand
function. See also Judd and Scadding (1982) for different perspectives.
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FIG. 3.1: CHOW TEST
JAPAN, 1963: I - 1972: I
FIG. 3.2: CHOW TEST
JAPAN, 1974: II - 1989: I
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FIG. 3.3: CHOW TEST
KOREA, 1974: III - 1978: IV
FIG. 3.4: CHOW TEST
KOREA, 1981: I - 1989: I
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FIG. 3.5: CHOW TEST
INDONESIA, 1978: I - 1982: IV
FIG. 3.6: CHOW TEST
INDONESIA, 1984: II - 1989: I
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1971. The most apparent structural break can be observed during 1970. In fact, the null
hypothesis of no structural break can be rejected at the 5% level for these terms. Hence
the stability of the money demand function within first sub-period can be called into
serious doubt. For the second period, the same graph was shown in Figure 3.2. The test
statistics for the first few years reveals instability of the money demand function as well.
Hence as a concluding observation for the Japanese case, we should note that the
instability of the money demand function in the seventies is clearly revealed by these
sequential Chow tests.
(ii) Korea
The same charts for the first and second period are given in Figure 3.3. and 3.4.
For the fixed exchange rate regime, though a statistically significant structural break
was observed in 1974:III, it seems that money demand function was fairly stable for the
rest of the period. For the second period, we observe a possible break point in 1982.
However the null hypothesis of no structural break can not be rejected by these test
statistics at the 5% level. The rest of the calculated statistics do not indicate any serious
instability problem for the money demand function for Korea.
(iii) Indonesia
The results of the sequential Chow tests are shown in the Figure 3.5. and 3.6.
For the first sample period, a slight peak for the structural break can be noticed in 1982,
the values for the rest of the period are rather small. For the second period, a slight peak
can be perceived in 1986 as well, the calculated test statistics do not present any
instability problem of the money demand functions. However, it should be noted that
possible structural breaks coincided with balance of payments crises.
Thus, in terms of the sequential Chow test statistics, instability of the money
demand function is the most serious in Japan. For Korea and Indonesia, perhaps this
issue need not be taken too seriously20 example, Pesaran, Smith, and Yeo (1985).
3.4.2 CUSUM AND CUSUMSQ TEST
In addition to sequential Chow test, structural stability of the money demand
function is examined by the cumulative sum (CUSUM) and CUSUM of squares
20
For a review of various tests of structural stability, see, for example, Pesaran, Smith
and Yeo (1985).
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FIGURE 3.7 CUSUM TEST : JAPAN, FIRST PERIOD
Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
FIGURE 3.8 CUSUMSQ TEST : JAPAN, FIRST PERIOD
Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
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FIGURE 3.9 CUSUM TEST : JAPAN, SECOND PERIOD
Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
FIGURE 3.10 CUSUMSQ TEST : JAPAN, SECOND PERIOD
Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
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(CUSUMSQ) tests, originated by Brown, Durbin and Evans (1975). The CUSUM test
statistic is sum of the recursive residuals normalized by the standard error of the
residuals and the CUSUMSQ statistic is the sum of squared recursive residuals
normalized by the residual sum of the squared errors for the full sample period. In the
figures discussed below, critical values at 5% significance level, given by Harvey (1981),
are also shown. Although any departures from the bounds indicates misspecification of
time variation in the parameters, CUSUM test is insightful for detecting systematic
changes of coefficients, with CUSUMSQ test being useful for recognizing sudden
events.21
Let us review the calculated test statistics for each countries.
(i) JAPAN
The CUSUM and CUSUMSQ statistics are shown in Figure 3.7 - Figure 3.10.
For the first period, though CUSUM test statistics do not signify any structural problem,
CUSUMSQ statistics exceed the lower 5% bound from 1969:III through 1971:II,
indicating hazardous variations of the coefficients. For the second period, however,
although CUSUMSQ test statistics do not reveal any serious instability of the money
demand function, CUSUM test statistics exceed the upper 5% bound from 1987:III. This
finding provides another evidence of the instability of the money demand function for the
Japanese economy in this period.
(ii) KOREA
Test statistics are plotted in the Figure 3.11 - Figure 3.14. For the first period,
though Chow test suggests possible structural break in 1974, CUSUM test statistics
around 1977 almost touch the upper 5% bound. However, both statistics do not indicate
any serious structural changes otherwise. For the second period, as with sequential
Chow test, none of the serious instability of the money demand function was detected by
both test statistics.
(iii) Indonesia
Relevant test statistics are shown in Figure 3.15 - Figure.18. None of the
serious problem of the structural stability of the money demand function are suggested
by these figures. Thus as a summary, the CUSUM and CUSUMSQ test statistics largely
21
See Garbade (1977) for the power of these tests.
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FIGURE 3.11 CUSUM TEST : KOREA, FIRST PERIOD
Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
FIGURE 3.12 CUSUMSQ TEST : KOREA, FIRST PERIOD
Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
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FIGURE 3.13 CUSUM TEST : KOREA, SECOND PERIOD
Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
FIGURE 3.14 CUSUMSQ TEST : KOREA, SECOND PERIOD
Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
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FIGURE 3.15 CUSUM TEST : INDONESIA, FIRST PERIOD
Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
FIGURE 3.16 CUSUMSQ TEST : INDONESIA, FIRST PERIOD
Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
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FIGURE 3.17 CUSUM TEST : INDONESIA, SECOND PERIOD
Plot of Cumulative Sum of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
FIGURE 3.18 CUSUMSQ TEST : INDONESIA, SECOND PERIOD
Plot of Cumulative Sum of Squares of Recursive Residuals
The straight lines represent critical bounds at 5% significance level
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echoed with the results by the sequential Chow test. Moreover, the instability problem of
the money demand function in Japan and Korea for the first period is focused by these
statistics as well.
3.5 Further Examination of the Money Demand Function
In this section, several specification issues for the money demand function will
be investigated. First, the test statistic of normality against the alternative that the
errors have a distribution of Pearson family proposed by Bera and Jarque (1981) is
calculated. This test statistics is distributed as χ 2 ( 2) under the null hypothesis that the
error term is normally distributed. Table 3.2 summarizes the results of this test.
According to these test statistics, the null hypothesis of the normality of the regression
residuals is not rejected in any case.
As a test of orthogonality assumption, a test statistic proposed by Hausman
(1978) is calculated. This test statistic, comparing two estimators of Test statistics are
shown in table 3.3. Null hypothesis is, however, not rejected at the 5% significance level
in any cases.
Next the test statistic for homoscedasticity is given by a simple LM test of =0 in
the auxiliary regression,
E ( u 2t ) = σ t2 = σ 2 + γ ( y 2t )
(3.7)
The calculated test statistics, though rather limited, are shown in table 3.4 along with P
values. Though, null hypothesis of the homoscedasticity of the residuals is rejected at the
10% significance level in the second period for Japanese money demand function, those
test statistics were found to be insignificant.
Finally
to
examine
possible
first
order
autoregressive
conditional
heteroscedasticity in the residuals, ARCH test statistic is reported. Table 3.5 includes
test statistics which are distributed as χ 2 (1) . According to the test statistics, the null
hypothesis are rejected in both periods for Korea and the second period for Japan.
Since possible first order autoregressive conditional heteroscedasticity was
detected by these statistics, the money demand function for those periods are
reestimated
assuming
first
order
autoregressive
conditional
heteroscedasticity.
Estimated result of the second period for Japan is presented in the table 3.6.22 The result
at large echoes with the previous one.
22
By the direct estimation of ARCH money demand function for Korea coefficients in the
autoregressive processes in the variances were found to be insignificant.
- 103 -
TABLE 3.2: NORMALITY ASSUMPTION
Country
Japan
Estimation Period
1966:II
1973:II
1973:IV
1980:I
1977:III
1983:III
1973:I
1990:I
1979:IV
1990:I
1983:IV
1990:I
4.321
1.390
.511
1.601
2.413
.354
.115
.499
.774
.588
.299
.838
BERA-JARQUE STATISTICS
P-VALUE
Korea
Indonesia
TABLE 3.3: HETEROSCEDASTICITY TEST
Country
Japan
Korea
Indonesia
Estimation Period
1966:II
1973:II
1973:IV
1980:I
1977:III
1983:III
1973:I
1990:I
1979:IV
1990:I
1983:IV
1990:I
HETEROSCEDASTICITY TEST
.018
3.307
1.318
1.116
1.428
.001
P-VALUE
.893
.069
.251
.291
.232
.969
TABLE 3.4: ARCH TEST
Country
Japan
Korea
Estimation Period
1966:II
1973:II
1973:IV
1980:I
1977:III
1983:III
1973:I
1990:I
1979:IV
1990:I
1983:IV
1990:I
ARCH TEST
.836
5.203
4.835
6.252
.212
2.411
P-VALUE
.360
.023
.028
.012
.645
.121
- 104 -
Indonesia
TABLE 3.5: ARCH TEST
Country
Japan
Korea
Indonesia
Estimation Period
1966:II
1973:II
1973:IV
1980:I
1977:III
1983:III
1973:I
1990:I
1979:IV
1990:I
1983:IV
1990:I
ARCH TEST
.836
5.203
4.835
6.252
.212
2.411
P-VALUE
.360
.023
.028
.012
.645
.121
TABLE 3.6: ARCH MONEY DEMAND FUNCTION FOR JAPAN
COUNTRY
PERIOD
JAPAN
1973:III-1990:I
CONSTANT
GNP
INT
M/P(-1)
R2
DW
-1.116
(-3.91)
.252
(4.550)
-.025
(-5.178)
.837
(23.968)
.998
1.27
See Table 3.1.
- 105 -
3.6 Different Specifications for the Money Demand Function for the Philippines
We found out that the interest rate variable in the money demand function was
entirely insignificant for the Philippines. Two interpretations may be possible: first, the
yields of TB may not be relevant interest rate variable for the money demand function, so
we may seek alternative measures of the interest rates for this economy. However, at this
moment only the discount rate, lending rate, and deposit rate are available for this
country. Among these yields, TB rate still seems to be the more appropriate interest rate
variables. Second, as mentioned in the introduction of this chapter, other factors, such as
substitution between money and real capital, foreign currency, and foreign bonds may be
relevant for this economy.
Okuda (1991) estimated the money demand function for the Philippine economy
using annual data. He estimated money demand function, using both M1 and
quasi-money, explicitly taking into account of considerations mentioned above; he
incorporated actual inflation rate and nominal yields of U.S. treasury bills in the money
demand function.
However, we constructed the expected inflation rates, calculated by the AR
model for the inflation rate process. This expected inflation rates were taken to be a
relevant variable for the consideration of the substitution between money and the real
assets.23 Furthermore, to capture the substitution between domestic money and foreign
currency, the expected rate of change of the exchange rate is formulated, using the
estimated AR model for the exchange rate process. Finally, the expected rates of return
for U.S. TB, that is the yield of U.S. TB plus the expected appreciation in the foreign
currency, were constructed to incorporate capital mobility. With these new variables, the
money demand function was re-estimated and the estimated results are shown in Table
3.7.24
According to these results, it turned out that only expected inflation rate was
significant at the 5% level. This result is robust with the inclusion of the other variables,
as shown in the table. Though our reestimation does not reveal statistical significance
of the foreign variables in the money demand function, Cuddington (1983) also reports
23
Of course, alternative position on this issue is that the impact of inflationary
expectations should be already incorporated into nominal rate of interest rates
24
For first-order serial correlation of the residuals, the maximum likelihood method is
used.
- 106 -
TABLE 3. 7: MONEY DEMAND FUNCTION FOR PHILIPPINE
COUNTRY
PERIOD
PHILIPPINE
1981:II - 1990:I
CONSTANT
GNP
M/P(-1)
EINF
-.265
(-.400)
.201
(.267)
.185
(.245)
-.517
(-.785)
-.438
(-.664)
.322
(3.038)
.334
(2.472)
.322
(2.848)
.379
(3.369)
.336
(3.234)
.888
(12.611)
.843
(10.378)
.836
(10.603)
.884
(12.984)
.895
(13.029)
-.006
(-2.461)
EER
EUSTB
ρ
R2
DW
.998
1.978
.997
1.939
-.002
(-.728)
-.475
(-2.851)
-.373
(-2.137)
-.373
(-2.132)
-.533
(-2.728)
-.517
(-3.138)
.997
1.930
.999
1.996
.999
1.995
-.002
(-.677)
-.008
(-2.728)
-.007
(-2.612)
.005
(1.216)
.003
(.002)
- 107 -
Notes: GNP=real GNP, INT=interest rate, M/P(-1)=lagged value of real money balance.
EINF=expected inflation rate, EER=expected rate of change of exchange rate.
EUSTB=expected rate of return of TB of the United States.
R2=adjusted R2, DW=Durbin-Watson statistics.
Numbers in the parentheses indicate t values.
that inclusion of foreign assets produced rather disappointing results for U.K., Canada,
U.S. and Germany; he only found slight evidence of capital mobility for U.S. and currency
substitution for Germany.
However, whether this reflects substitution between money and real assets can
still be questioned. The possible correlation between the expected rate of inflation and
the expected rate of change for the exchange rate may simply explain the result.
Furthermore, even if this result is convincing, then a more serious problem with the
conduct of the monetary policy now comes in. It is easy to see that the transmission
mechanism for the money supply in this result, is different from that of traditional
money demand function. In any event, since our time series models used in this section
are rather naive, our finding is not final.
3.7 LAG STRUCTURE OF MONEY DEMAND FUNCTION
So far in this chapter, the specification of the money demand function was
justified by the partial adjustment hypothesis. However, this hypothesis has been
criticized on many grounds. For example, the adjustment mechanism implicit in
equation (3.6) will yield continuous under or over-prediction of short-run money demand
over long-run money demand.25 It is conceivable for speed of adjustment coefficient
dependent on economic variables such as levels and/or variability of income and interest
rate. Moreover, the symmetrical assumption in the cost function (3.2) can be questioned.
It may be much more costly to be below the long run equilibrium position. Furthermore,
compared with the cost function for the investment, the cost function in this context
might lack solid microeconomic foundation. Though this framework incorporates
divergence of the short-run money balances from the long-run equilibrium position, the
disequilibrium adjustment in interdependent asset markets are ignored and so on.26
It is well-known that the lag structure in the money demand function, such as
(3.6), can be justified by the adaptive expectation mechanism as well. Let us briefly
review the argument. Suppose that the long run money demand is determined by,
(3.8)
M t = η y Yte − η r rt
That is, the long-run money demand is determined by the expected income, Y et , and
25
Higher order adjustment lag structure can mitigate, in principle, this problem.
26
For a tractable interdependent asset adjustment model, see Friedman (1977).
- 108 -
nominal interest rate (regarding interest rate, no adaptive expectation mechanism is
assumed here). Concerning the expectation formation, the adaptive expectation
formation,
Y et − Y et −1 = θ ( Yt − Y et −1 )
(3.9)
is assumed. Substituting (3.9) into (3.8) results in,
M t = η yθ Yt − η r rt + (1 − θ ) rt −1 + (1 − θ ) M t −1
(3.10)
Unlike the case of the partial adjustment case, the lagged value of the interest rate
enters the money demand equation in this specification of the adaptive expectation
hypothesis.27 Though, the adaptive expectation hypothesis is only optimal in somewhat
restrictive circumstances, we rather focus its relationship with partial adjustment
hypothesis.
It is well-known that including lagged value of the dependent variables in the
money function greatly enhances the empirical tractability of the actual data. If equation
(3.10) is a true money demand function, then the money demand function in the form of
(3.6) would “work” well and vice versa. 28 The lag structure of the money demand
function is loosely defended on the grounds of either the partial adjustment mechanism
or the adaptive expectation hypothesis mentioned above. It is fair to say that the
relationship between these two mechanism are not seriously focused. However, we can
not exclude the possibilities of coexistence of those two mechanism apri-ori. If coexistence
happens to be the case, then the estimated money demand function based on solely one
mechanism will surely have biased estimates. However, we rather take this largely
ignored “mere” theoretical possibility in detail here because coexistence of two
mechanism may shed the some new insight for the instability of the money demand
function as well. It is in our interest to examine the possibility of co-existence of these
mechanisms in detail for the four countries of our study.
In any case, combining these two hypotheses together consists of a system of
27
In addition to this, there are two more differences between these two formulae. First,
although the money demand function derived by the partial adjustment hypothesis is
just identified, the one with adaptive expectation assumption is over-identified. Secondly,
the error structure of the latter function is first order moving average process, while the
former is not.
28
See, Goodfriend (1985) on this point.
- 109 -
equations,
(3.11)
M *t = η y Y et − η r rt
(3.12)
Y et − Y et −1 = θ ( Yt − Y et −1 )
(3.13)
M t − M t −1 = γ ( M*t − M t −1 )
Substituting (3.12) and (3.13) into (3.11) results in a final nonlinear equation to estimate,
(3.14)
M t = γ η yθ Yt − γ η r (1 − θ )rt −1 + ( 2 − γ − θ )M t −1 − (1 − θ )(1 − γ )M t −2
This issue was first focused by Feige (1967). Equation (3.14) is interesting in the
following senses; first if the true money demand function is indeed (3.14), then the
popular reduced form money demand function,
(3.15)
M t = δ1 + δ 2 Yt + δ 3 rt + δ 4 M t −1
will have biased estimates. Moreover, the instability of the money demand function in
the form of (3.15) could be related with the omitted variables, rt −1 and M t −2 . Second,
the stability of the reduced form money demand function based on (3.14),
(3.16)
M t = δ1 + δ 2 Yt + δ 3 rt + δ 4 M t −1 + δ 5 M t −1 + δ 6 M t −2
can be analyzed in terms of the underlying parameters; long-run equilibrium
coefficients of the money demand function, η y , η r , a short-run partial adjustment
parameter γ and an expectation adjustment parameter θ . In the context of the
rational expectations hypothesis, it can be argued that the instability of the
conventional form of the money demand function might be caused by the changes in the
adaptive expectation coefficient, θ , and/or changes in the speed of adjustment, γ , with
other “structural” parameters being unchanged. Since this phenomenon is obviously
related to the well-known Lucas critique, it is quite interesting to see if the mere
theoretical possibility should be dismissed empirically. Since demand function (3.16) is
due to changes in η y , η r or changes in γ and/or θ , joint estimation of the income
expectation generating equation and the money demand function, if fully implemented,
may provide some insights on the stability issue.29 Cuthbertson and Taylor (1990)
29
However, it can be argued that these “theory” based models, even with some
empirical success, would still be too simple-minded with “true” complex dynamic
- 110 -
indeed estimated such a model, in spirit, for the U.S. data under the hypothesis of the
rational expectation and the multi-period cost function. They examined the hypothesis
that shifts in expectations formation function caused the instability of the money
demand function, not the changes of the underlying long-run money demand function
and the results seems to be promising.30
In any event, since equation (3.14) is an over-identified equation, Feige
estimated this equation for U.S. annual data from 1915-1963, using constrained
nonlinear two-stage estimation procedure. He found the results, γ =1.091, θ =0.239 for
M2. Hence instantaneous adjustment is concluded. However, Laidler and Parkin (1970)
found two local maxima by searching over the values of θ and γ in the range (0,1) for
U.K. quarterly data. One maxima was with large value of θ and small value of γ and
the other was an opposite combination. They were in favor of short adjustment lags.
Mayer and Neri (1975) also adopted searching method and found out both mechanism
were operative for the annual U.S. data from 1897 to 1960. Furthermore, using
maximum likelihood estimation method, Thornton (1982) also found out that both
mechanism were operative, using U.S. quarterly data from 1952:2 to 1972:4. He revealed
that inclusion of adaptive expectation, did increase, otherwise implausibly slow,
adjustment of speed. In any event, as a first step, we also adopt the searching method to
examine this equation in line with them.
The value of γ , the coefficient of partial adjustment is assumed to be 0.1,
0.2, ......, 0.9. Given those values, equation (3.14) is estimated by non-linear estimation
method. Estimated results that maximized the log likelihood function were shown in
Table 3.8.31
For Japan, the adaptive expectation mechanism is not instantaneous for both
periods. As in the standard analysis, the speed of adjustment is considerably slow.
Interest rate elasticities are excessively high. Adaptive expectation coefficients are
relatively high in the first period for Korea. However, the speed of adjustment is
structure revealed by, say, “error correction” models. See Cuthbertson and Taylor (1992)
on this.
30
See also, Cuthbertson and Taylor (1987), (1989).
31
However, maximum likelihood estimation of the model, taken into account of the first
order serial correlation of error term, should be fully implemented.
- 111 -
TABLE 3.8 : MONEY DEMAND FUNCTION REVISITED
COUNTRY
JAPAN
JAPAN
KOREA
KOREA
- 112 -
INDONESIA
INDONESIA
PERIOD
LON RUN
LOHG RUN
COEFFICIENT
COEFFICIENT
INCOME
INTEREST RATE
OF EXECTAION
OF PARTIAL
ELASTICITY
ELASTICITY
ADJUSTMENT
ADJUSTMENT
1.036
.532
.688
(.017)
(.104)
(.104)
1.043
.535
.720
(.008)
(.076)
(.1000)
1.064
.249
.881
(.076)
(.204)
(.162)
1.084
.308
.825
(.018)
(.127)
(.116)
.862
.123
.604
(.094)
(.219)
(.153)
1.043
.536
.720
(.008)
(.076) 2.188)
(.100)
ρ
R2
DW
.1
.118
.998
2.484
.1
.13
.998
1.551
.5
.276
.963
2.283
.2
.231
.996
1.286
.1
.161
.986
1.723
.1
.282
.982
2.248
1962:II-1973:I
1973:III-1990:I
1973:IV-1979:IV
1980:II-1990:I
1977:II-1983:IV
1983:II-1990:I
Notes: Numbers in the parentheses indicate standard error.
surprisingly high for this period.32 For the second period, the results are similar to those
of Japan. Nonetheless, interest rate elasticity is considerably lower. Basically the results
for the Indonesian case echo with other results; both mechanism are at work for both
periods.
On the whole, adaptive expectation mechanism and partial adjustment
mechanism are simultaneously operative for most of the cases. Multiple local maxima
were not found here. Moreover, it is worth noticing that the income elasticity of the
long-run money demand function in this formulation is invariably close to one, excepting
for the first period for Indonesia. Compared with the calculated long-run income
elasticity in Table 3.1, these figures deserve attention. Especially long-run income
elasticities for the Indonesian economy became smaller than half of the standard
analyses. Those results seem to be in favor of a research agenda on the instability of the
money demand function from this new perspective.33
These results are tentative, however, besides the issue of the unit root and
cointegration, 34 the specification of the money demand function may still demand
further thorough inquiry.
3.8 Concluding Remarks
In this chapter, we investigated the most important macroeconomic function in
the conduct of the monetary policy, the money demand function.
We found simple partial adjustment model a la Goldfeld to be reasonably solid
in terms of sign conditions, significance of the estimated coefficients, except for the
Philippines. The puzzling results for the Philippines could be due to the inappropriate
choice of the interest rate variable or the different monetary transmission mechanism
prevailing in the economy. Though we found the result in line with the second possibility,
this awaits further investigation.
Furthermore, we examined the instability issue of the money demand function
within a given sample period. We used a sequence of Chow test to take into account of
the conceivable multiple break points. According to the calculated test statistics, this
32
This result is rather suspicious because in fact maxima is found with the value of
coefficient of expectation adjustment larger than one.
33
For a related analysis on this topic see Cuthbertson (1985).
34
The issue of unit root and the cointegration are examined in Appendix 1.
- 113 -
issue of the instability of the money demand function is most serious for Japan. For the
other countries, though a slight possibility for the possible single break point within a
given sample period was found in the several results, this instability problem need not be
taken too seriously. However, the suggested possibility of the co-existence of the partial
adjustment mechanism and adaptive expectation mechanism may require further
efforts.
- 114 -
Chapter 4. Results of the Monetary Policies of Four Asian Countries VAR Analysis
4.1 Introduction
In this chapter, we applied the Vector Autoregressive Model (VAR Model) to examine
the effects of the monetary policy of the four Asian countries concerned. The advantage of
applying the VAR Model is that we can examine the results of economic activities without
constructing large-scale econometric models. Thus we will be able to obtain a base for
comparative study by using identical model structures with similar data sets.
The VAR model is advocated by Sim’s celebrated study of 1972 and 1980. He
challenged the conventional econometric methodology by criticizing its use of “incredible”
identification restriction. He then claimed that unrestricted vector autoregressions (VAR) can
provide better instruments for understanding interrelationships between important
macroeconomic variables, such as money, interest rate, prices, and output.
Since the VAR model, variance-decomposition, and impulse response function are all
well-known and widely practiced in many studies, let us briefly review the basic methodology.
Starting from the vector autoregressive representation,
(4.1)
X t = b( L )X t + e t
where x t is a stationary stochastic process and L is the lag operator. However, most VAR
analyses are based on vector moving average representation due to the Wald decomposition
theorem,
(4.2)
X t = n t + a( L )e t
E(et ) = 0
E(et e t− k ) = W,
k >0
= 0,
k =0
& , ∆ R, ex& , ZBP)
e r = (g& , &i , pc&, pw
where n t is assumed as perfectly predictable. The vectors of errors are the forecast error of
the autoregression given information available at t-1 if the roots of a(Z) lie outside the unit
circle. To obtain impulse response function and variance decomposition normalization is
necessary. To examine cumulative quantitative response of an element of x t to an
unpredicted innovation of some component of e t , components of e t must be orthogonal.
- 115 -
However, matrix W is not always likely to be diagonal. Hence artificial division of the
covariance of the residuals enables the errors to be orthogonal. The usual convention is some
particular ordering of the residuals, and then variance decomposition of the k-step-ahead
forecast error is the proportion of the total variance of one component of x t caused by shocks
to the moving average representation of another variable.1
In our study, we must note that there are difficulties in obtaining the economic
interpretation by the results of our VAR model. Data we applied in our VAR Models are the
difference between current and previous periods of each variable, except interest rate, to
avoid non-stationary feature by the existence of the unit root.2 However, because we applied
the difference model, we faced the difficulty of interpreting the economic meanings directly by
observing the sign of the coefficients of the estimated equation of VAR Model and the
adjustment process depicted by impulse response function. Particularly, the variance
decomposition applied in this chapter is based on the Cholesky decomposition, as in the
traditional approach applied in Sims (1972). There exists a weak point of the traditional VAR
approach that results depend very much on the ordering of the variables in the model.
Another weak point of this analysis is that the structure of the model cannot be determined,
and to overcome this difficulties we must apply Structural VAR Model which has been
recently developed, but this issue is left for the future study.
Using the VAR Model, we will investigate monetary policies of Japan, Korea,
Indonesia, and the Philippines. Variables we chose were: real GNP, wholesale price index, M2,
and the level of interest rate (basically money market rate). The aim of this study is to
determine whether money is an endogenous variable or exogenous variable. If one variable
can be identified as exogenous variable from the variance decomposition analysis, we defined
this variable as an operational variable, which is a “policy variable.” If money or interest rate
can be defined as “policy variable,” and they have effect on other variables, we interpreted
that “fine tuning” type of macroeconomic monetary policy was implemented. From this
analysis, the equation on money can be assumed to be “money supply function.” However, by
applying VAR approach based on Cholesky decomposition as mentioned above, and using
difference of the data on the independent variables makes the sign of the estimated
1
See Lutkepohl (1991) for detailed discussion of VAR model, its estimation, and related
issues. See Cooley and Leroy (1985) for methodological criticism for the VAR analyses as well.
2
For the issue of the time trend in VAR model, see Runkle (1987). See also Todd (1990) for
related issues regarding the robustness of the Sim’s original result.
- 116 -
equation ambiguous. This means that we cannot precisely identify whether the estimated
results are money demand or money supply function.
In applying variance decomposition, contribution of percentage of forecasting errors
in the second and twentieth periods are shown in the attached Tables 4-1 to 4-12.3 We will
basically consider the percentage contribution in the twentieth period in picturing the
causality between the variables in Figures 4-1 to 4-12. In this study, as well as in the other
studies, there is no clear criteria for measuring the strength of the innovation of one variable
to the other variables by using variance decomposition. In this study, we pick up the
contribution of variance decomposition exceeding 10% level as the existence of “causality,” as
has been done in many previous studies.
We divided the estimation period into two parts, coinciding with the estimation
period of the reaction function and money demand function but slight differences appeared
due to the seasonal adjustment of the data set. The division of the period was made by the
difference of the exchange rate regime in the cases of Japan and Korea. The estimation period
of Indonesia is divided by the constraints in the continuity of the data set.
4.2 Japan4
(1) Period I: From 1962 Q3 to 1973 Q1 (Fixed exchange rate regime)
The percentage of variance decomposition of forecasting error is shown in Table 4-1.
On the innovation of the interest rate, 75% of the shock was caused by the innovation itself,
while the contribution of the money supply and real GDP growth accounted for 14% and 13%
respectively. Innovations of money supply have an effect on their own variables by 92%, a
result which suggests the strong exogenous feature of the money supply. That the money
supply does not have strong impact on inflation is shown by the 9% contribution of innovation
to the forecasting error. Inflation can be considered as an independent variable. Contribution
of the interest rate innovation of real GNP growth is 11%.
The results of variance decomposition indicate that interest rate had strong effect on
3
Although all of the results are not shown here, the issue of the particular ordering did not
cause serious problems in our calculations.
4
For empirical analyses applying VAR method for the Japanese economy, see, for example
Kunitomo and Yamamoto (1986). Kawasaki (1991) also applied Bayesian VAR for the
Japanese economy. Analyses of structural VAR model contains Iwabuchi (1990) and Kitasaka
(1993).
- 117 -
real GNP growth, and vice versa. This result could indicate that the authorities were trying
to adjust fluctuation of business cycle by controlling interest rate, but at the same time, the
interest rate was being affected by the fluctuations in the business cycle.
Many interpretations could be considered on the mechanism of interest rate
affecting real GNP growth. One possible route may be through the effect on equipment
investment, while real GNP growth affects interest rate by the shift in money demand
function. Even so, we can interpret that authorities were carefully trying to control real
economic growth by influencing the interest rate, but at the same time considering the effect
on interest rate from the real economic growth. In traditional terminology, we might be able
to call this policy “stop-and-go policy” or “fine-tuning policy.”
We can only observe a limited effect of money supply on interest rate and innovation
of money supply does not have any impact on the rest of the variables.
(2) Period II: From 1973 Q1 to 1990 Q1 (Flexible exchange rate period)
The percentage of the variance decomposition of the flexible exchange rate period is
shown in Table 4-2. On the interest rate, 65% of forecasting error is attributed to the interest
rate itself, and 26% is attributed to the inflation rate. On the money supply, contribution of
its own variable is 82%, and this dominates the effect of other variables. Innovation of
interest rate contributed 21% to the shock on inflation, but contribution of its own variable is
60%. Interest rate has 10%, and the rate of inflation has 15% contribution respectively on the
shock on real GNP growth.
The outstanding characteristics of the second period were that the relationship
between interest rate and real economic growth weakened and relationship between interest
rate and inflation rate became stronger. One candidate for this explanation is the existence of
Fisher Effect, due to growing inflationary expectations since the high inflation period in the
mid-1970’s.
Another view explaining the strong mutual relationship between inflation rate and
the interest rate is the effect of exchange rate fluctuations in the second period. Since the
exchange rate is closely related to the inflation rate, the interest rate is affected by the
fluctuation of the exchange rate. To examine this hypothesis, we replaced the exchange rate
by wholesale price index, and re-estimated the VAR Model. As a result, percentage of the
variance decomposition of the forecasting error shows that 38% of shock on the interest rate
is attributed to the fluctuations of the exchange rate. Also, we can see that the interest rate
has a strong effect on the real GNP growth.
This result suggests that the interest rate was strongly affected by the fluctuation of
the exchange rate in the flexible exchange rate period. Importance of the capital flows in the
- 118 -
• JAPAN
Figure 4-1
Period I
(1962Q3 - 1973Q1)
Figure 4-2
Period II
(1973Q2 - 1990Q1)
Figure 4-3
over 20% contribution on variance decomposition
over 10% contribution on variance decomposition
(
) F-statistics (block exogeneity)
[
] Schwarz-Bayes Information Criterion
- 119 -
flexible exchange rate regime might be the background of this result. However, the effect of
inflation on the real GNP growth disappeared in period II while the effect of interest rate on
GNP growth retains its significance. This result suggests that the Fisher Effect is not robust,
and still even in this period, the major tool of the monetary policy was controlling the interest
rate.
(3) Comparison between the two periods
In both periods, we were able to observe the strong effect of the interest rate on the
real economic growth which indicates the significance of the monetary policy.5
Another interesting result can be obtained by checking F-statistics of each VAR
estimation. Comparing two periods, F-statistics on all variables improve in Period II. This
result indicates that the mutual relations between variables are increasing. Also, if we
compare Schwartz-Bayes Information Criterion for the two periods, the value is decreasing in
Period II, which shows that the significance level of VAR estimation itself is improving. This
means that the relationship between variables which indicate macroeconomic performance,
and the variables which are the tools of the monetary policy, are growing closer in the second
period.
4.3 Korea
We also divided the estimation period into two parts for Korea. In the first period,
from third quarter of 1970 to fourth quarter of 1979, Korea adopted the fixed exchange rate
system. In the second period, from second quarter of 1980 to first quarter of 1990, the
exchange rate regime changed to a flexible rate system. However, the definition of this
exchange rate system is not exactly the same as the flexible exchange rate system adopted in
Japan. The exchange rate control system of Korea was so called “dirty float” system, where
the exchange rate was not solely determined by the market, but actually determined by the
monetary authorities.
(1) Period I: 1970 Q1 to 1979 Q4
The percentage of variance decomposition of the forecasting error of the first period
is shown in Table 4-4. A remarkable result of this period is, from the variance decomposition,
we only deduced that the money supply had contributed 13% to the shock on real GNP
5
These observations are not apparently inconsistent with the results of the existing studies
mentioned in footnote 4.
- 120 -
growth. We obtained no other results from variance decomposition showing interrelation
among variables.
The interest rate, which is the discount rate did not affect either on real economic
growth or inflation, and these variables show strong exogenous feature. Also, concerning the
shock on the interest rate, contribution to innovation of money supply is 3.3%, while the
contribution of interest rate to money supply is 1.6%. These results can be interpreted from
the well-known character of the financial policy that interest rate was used as a tool for
rationing subsidized credits by the financial institutions, and was not used as a tool of
macroeconomic adjustment.
Contributions of money supply to real GNP growth and inflation were 13% and 9%
respectively. This result may indicate that the monetary authority was providing money
supply with considerations for real economic growth. We might note that Shin (1978) applied
Granger and Sim’s causality test for the Korean Economy from 1962:Q2 to 1977:Q2. Though
he failed to find an explicit result concerning causal relationship between money supply and
nominal GNP, he found that money had unidirectional influence on real GNP and the GNP
deflator.6 Our result seem to be consistent with his claim.
One interesting feature of this period was the contribution of money supply to
inflation. In this analysis, we can examine the causality between the variables, but it is
difficult to determine the sign of the causality as we have explained at the beginning of this
chapter. However, this result seems to be insinuating that the cause of the inflation was
related to the supply of money.
(2) 1980 Q1 to 1990 Q1
As we can see in Table 4-5, in this period, the relationship between variables shown
by variance decomposition, is strong between interest rate (money market rate) and inflation.
To the shock of interest rate, contribution of inflation is 12%, while interest rate contributes
11% to inflation.
The relationship between money supply and interest is weak. Contribution of
interest rate to money supply is 5% while contribution of money supply to interest rate is 2%.
Along with strong exogeneity of the interest rate, these results suggest that, even in 1980’s,
credit rationing had strong impact on the monetary policy.
We can interpret the relationship between interest rate and price in two different
ways, as we have done in the case of Japan. First, we will focus on the possibility of the
6
He also carried out same tests for the United States, Canada, and the United Kingdom.
- 121 -
existence of expectations of inflation, known as the Fisher Effect. Another explanation is to
stress the effect of the exchange rate on interest rate, which could have been hidden in the
rate of inflation.
To examine the relationship between interest rate and exchange rate, we replaced
wholesale price index by nominal exchange rate and re-estimated the VAR Model, The results
of the variance decomposition are shown in Table 4-6; they suggest that there is causality
from interest rate to exchange rate, which indicates that the authorities started to determine
the interest rate, not only focusing on the credit rationing, but also considering the effect of
exchange rate.
When we consider the importance of credit rationing of the subsidized credits in
Korea, the discount rate might have greater effect on macroeconomy than in the other
countries. Therefore, we replaced money market rate by discount rate and re-estimated the
VAR equation. We can see the strong influence of the discount rate from variance
decomposition of forecasting error in Table 4-7. The discount rate contributes 13% to the
forecasting error of the money supply, 28% to the inflation rate, and 13% to the real economic
growth respectively. This result indicates that the impact of the interest rate on the
macroeconomic variables became stronger in the 1980’s.
Finally we will note results of VAR analysis by Kim and Oh (1991). They also
estimated four variable VAR models for the Korean economy for the period from 1976:III to
1990:I. Their results differ considerably from ours. First, they used the interbank call rate,
curb market rate, and the lending rate of the commercial banks as a short-term interest rate
indicator. Although the variance decomposition result itself with these short-term interest
rates was not apparently shown in the paper, they mentioned that only 2-7% of the variance
of the growth rate of GNP can be attributed to shocks in short-term interest rates. However,
they examined the corporate bond yield and the treasury bond yield as representative longterm interest rates as well. They found that for a period of 20 quarters, about 10-12% of the
variance of the GNP growth rate can be attributed to the shock of long-term interest rates.
More surprisingly, approximately 27% of the variance of the real GNP growth can be
attributable to the shocks in the growth rate of M2. Our results using short-term interest rate
indicate that innovation of the interest rate had much smaller impact. Moreover, the longterm interest rate seems to be significant in explaining the growth of real GNP. However, the
effect of innovation of money supply with long-term interest rate is not comparable to ours.
Although the sample period is rather different, further investigation of this difference is
clearly in order.7
7
They also estimated VAR model incorporating consumption and investment to study
- 122 -
• KOREA
Figure 4-4
Period I
(1970Q3 - 1979Q4)
Figure 4-5
Period II
(1980Q2 - 1990Q1)
Figure 4-6
Figure 4-7
- 123 -
(3) Results of Korean monetary policy
All the results mentioned above seem to be quite different from other countries. If we
thoroughly investigate the monetary policy of Korea, we might have to consider the following
other factors on top of the traditional study. First we can point out the importance of credit
rationing, wherein interest rate and money supply were determined without the
consideration of the macroeconomic conditions, but rather used as an integral part of the
industrial policy. Second is the existence of large-scale unofficial financial markets, not
controllable by the monetary authorities, which caused a leakage which cannot be ignored in
controlling monetary aggregates. The third factor is the method of managing the target of the
monetary aggregates. According to Korean authorities and a number of scholars, the target of
the monetary policy of Korea was set at the growth rate of M2. However, the method of
accomplishing this target was peculiar considering traditional monetary policy. When the
growth of M2 exceeded the target, Korean monetary authorities influenced the banks to
reduce M2 by shifting term deposits to saving deposits. As a result, by the shift of funds from
M2 to M3, target of the growth rate of M2 was fulfilled. This must be clarified empirically, but
suggests the peculiarity of the monetary policy in Korea.
Despite these results, the comparison of F-statistics of VAR estimation in Period I
and Period II, reveals the importance of the monetary indicators in the Korean economy. Also,
from Schwartz-Bayes Information Criterion, the significance of estimation of VAR analysis
itself remained at the same level.
4.4 Indonesia
Despite the non-economical reason for division of the estimation period, a number of
interesting issues can be pointed out by comparing the results of the two periods. The most
significant feature of the economy of the first period was the heavy dependence on oil
production, while in the second period, that dependence gradually declined. Also,
deregulation of the financial market started in 1983 seemed to have an impact on the
monetary policy and economic performance.
(1) Results of the period of 1977 Q1 to 1983 Q2
The percentage of variance decomposition of forecasting error is shown in Table 4-8.
On the variance decomposition of interest rate, 95% of forecasting error is caused by the
potential effect of the long-term interest rate on those two main demand components.
- 124 -
innovation of own variable and shows strong exogeneity. Money supply has the dominant
effect on the forecasting error of own variable, while the innovations of the other variables do
not have significant impact on the money supply. The shock on inflation rate shows that 73%
of the forecasting error is caused by the innovation itself, and 20% is by the innovation of the
money supply, and other variables do not have significant effect. The variance decomposition
of forecasting error of real GDP growth shows interesting results. The innovation of own
variable contributes to 76% of the forecasting error of real GDP in the second period but this
effect declines to 52% in the twentieth period. On the other hand, innovation of inflation rate
contributes to the forecasting error of real GDP growth.
From the characteristics of the Indonesian economy in this period, we must consider
that the oil price strongly influenced the general price level. Therefore, we replaced wholesale
price index by oil price ($US/BBL) and re-estimated the VAR Model. The results of the reestimation in Table 4-9 show strong impact of oil price on real GDP growth rate and growth of
money supply. When we look at the contributions of oil price on real GDP growth, 46% in the
second period increases to 74% in the twentieth period, and dominates the effect on the shock
of economic growth. We also can observe the shock on oil prices affecting the money supply.
We can interpret the results in a straight-forward way that rise of oil price increases the
export earnings dramatically, and this impact causes the increase of money supply. We
confirmed that dominant factor of the Indonesian economy in the first period was oil and the
impact of monetary policy was negligible.
(2) Results of the period 1984 Q3 to 1989 Q2
The percentage of variance decomposition of forecast error is shown in Table 4-10
and the relation of causality is shown in Chart 4-10. On the interest rate, the innovation of
own variable contributes 89% and dominates the contribution of other variables. On money
supply, we can see innovation of its own variable contributes 83% and inflation contributes
17%. On the inflation rate, innovation of money supply and real GDP growth have effects of
31% and 11% respectively, while 67% is due to its own shock. On real GDP growth rate, 50%
of the shock is caused by the innovation of its own variable, while 26% is due to interest rate
and 14% is due to money supply.
The most significant result of the second period is that the impact of inflation on real
GDP weakened, which showed a 9% contribution on forecasting error. Also, both effects of
money supply on real GDP growth and effect of interest rate on real GDP growth became
apparent. Since the middle of 1980’s, the role of oil in the Indonesian economy relatively
declined, a result which was reflected in declining effect of inflation rate on real GDP growth.
The interest rate started to have significant effect on real GDP growth, possibly suggesting
- 125 -
• INDONESIA
Figure 4-8
Period I
(1977Q1 - 1983Q2)
Figure 4-9
Figure 4-10
Period II
(1984Q3 - 1989Q2)
Figure 4-11
- 126 -
that the effect of financial liberalization is beginning to reveal its impact on the
macroeconomy in the late 1980’s. 8 However, this interpretation is defective, since the
relationship between money supply and interest rate is weak.
To confirm the effects of the oil price in the second period, we replaced wholesale
price index by oil price ($US/BBL), just as in the first period. The interesting result was that
contribution of innovation of oil price was not strong as in the first period, and causality
among other variables remains basically the same. This may indicate that in the case of
Indonesia, impact of oil price declined and the effect of interest rate and money supply
became stronger.
(3) Comparison of two periods
As we have seen in the results mentioned above, in the first period, we saw clearly
the dominant effect of oil on the economic performance of Indonesian economy. In the second
period, we can see that the effect of oil declines and effect of the monetary variables increased.
Even though we deduced that the impacts of monetary variables are increasing, it was
difficult to point out the result of the financial liberalization by the VAR analysis. Most
F-statistics on the VAR estimation increase in the second period which suggest the
significance of the relationship between economic performance and monetary policy. However,
significance of the VAR estimation itself measured by Schwartz-Bayes Information Criterion
declines in the second period.
4.5 The Philippines
The period we applied the VAR analysis in the case of the Philippines is third
quarter of 1982 to first quarter of 1990, corresponding to the estimation periods of the
reaction function. The percentage of the variance decomposition of forecasting error is shown
in Table 4-12. Contributions of growth of money supply to interest rate, inflation rate, and
real GNP growth are 38%, 58%, 13% respectively, showing that money supply has strong
impact on other variables. Inflation rate also contributes 20% to own variable and 30% to real
GNP growth.
The results of the VAR analysis of the Philippines are difficult to interpret. One
extraordinary characteristic is the extreme passivity of the interest rate, which is strongly
influenced by money supply, but interest rate does not have significant influence on other
variables. The strong effects of the money supply on the rate of inflation and interest rate can
8
Alternatively, this might be due to the interest rate targeting procedure adopted since 1983.
- 127 -
• PHILIPPINES
Figure 4-12
(1984Q3 - 1990Q1)
- 128 -
be interpreted that the increase in money supply had a significant impact on inflation rate.
Again, one candidate explanation is the Fisher Effect in the highly inflationary economy,
when the expected inflation dominates the determination of nominal interest rate (Figure
4-12).
However, the result shows that interest rate does not have significant effect on real
economic growth, which indicates the lack of effective tools for adjusting the real sector. On
the other hand, the money supply has an impact on inflation and inflation has an impact on
real economic growth. We also can observe that the money supply has a direct impact on the
real economic growth. It seems that increase of money supply induces inflation and
stimulates economic growth, but the inflation tends to have a negative impact on real
economic growth. This result indicates that the supply of money has contradictory effects on
performance of Philippine economy, that is, the coexistence of high inflation and low or
negative real economic growth.
4.6 Interpretation of VAR analysis
We obtained interesting findings on the one hand, while we are disappointed on the
other. In this final section, we will point out general findings of this analysis and will identify
issues which might be interesting subjects for further study.
As we have explained earlier, many studies apply VAR, and most of them obtain
different results from ours. The detailed comparison of these studies would be an interesting
topic of the further study, but we will not attempt that comparison in this paper. 9 Currency
in circulation, monetary base, M1, M2, and domestic credit were used to examine the role of
money. The results are quite different from our study possibly due to the difference of data for
Indonesia and the Philippines, and the different estimation periods for all countries. In
particular, quarterly GDP data is estimated from annual GDP data in the case of Indonesia
and the Philippines. The measuring periods are also different.
We can summarize the results of Hamann (1993) as follows. In the case of Indonesia,
none of the variables representing money and output Granger-cause the price. At the same
time, none of the variables representing money and price Granger-cause output, except for
9
When we were finalizing this chapter, we encountered a study of Hamann (1993), which
applies a VAR approach studying the relationship among money, output, and prices in
Indonesia, Korea, and the Philippines. The purpose of study was to investigate whether
money supply contains any advanced information of prices or output by examining by
Granger’s Causality Test.
- 129 -
the domestic credit. The relationships among money, price, and output by means of
Granger-causality are apparently not found. The differences between our study are that, in
our case, we can see strong relationship among variables in the case of Indonesia.
In the Korean case, currency, M2 and domestic credit Granger-cause the price, while
monetary base, M1 and M2 Granger-cause the output. In only one case, where the estimation
is made when M2 is adopted as the money variable, the price Granger-causes the output.
Price also Granger-causes the money supply when domestic credit was chosen as a variable
for money. Therefore money Granger-causes both price and output in almost cases. If the
exchange rate is included in the model, it Granger-causes price in almost all cases on VAR
model including interest rate. The results of our estimation show very weak relationship
between variables, which is quite different from this analysis.
Results in the case of the Philippines are as follows. Currency, M2, and domestic
credit Granger-cause the price, while price Granger-causes the output when M1 is adopted as
the money supply in the model. Therefore the existence of Granger-causality from money to
output is apparent. Furthermore, interest rates Granger-cause price in when currency or
monetary base were chosen as a variable of money. We also were able to observe exchange
rate Granger-causing the price. The results of this estimation are some what similarity with
our results. We also obtained the causality of money supply to price, and causality of price to
output. The significant difference was that in Hamann (1993) the causality was observed
from interest rate to price, while we obtained a opposite direction of causality.
(1) Monetary policy and exchange rate regimes
In the cases of Japan, Korea, and Indonesia, we divided the estimation period into
two parts. Japan’s first period is a clearly fixed exchange rate regime and the second period is
a flexible exchange rate regime. The first period of Korea was basically a fixed exchange rate
regime, but the second period was not exactly a flexible exchange rate regime but can be
considered to be a “dirty float” regime. Even the division of the estimation period of Indonesia
is based on statistical reasons, the first period of Indonesia can be considered to be a fixed
exchange rate system and the latter part of the second period is a “dirty float.”
In orthodox or traditional economic theory, it is commonly understood that the
monetary policy will become independent and effective in the flexible exchange rate system.
Representative, well-known applications are the Mundell-Fleming type of analyses. Or at
least there will be significant difference in the result of the monetary policy when there exists
a difference in the exchange rate regime of restriction on capital mobility. However, the
empirical findings from our four variable VAR estimations do not support this view. These
results may suggest that we will obtain interesting results if we examine with different
- 130 -
models to evaluate monetary policy under alternative circumstances.
(2) Increasing relationship between economic indicators and monetary variables
In the countries for whom we divided the estimation period into two parts, which are
Japan, Korea and Indonesia, F-statistics of each VAR estimation improved in the second
period. This result shows that the relationship between macroeconomic variables and
monetary variables are increasing and suggesting that background of effectiveness of the
monetary variables is increasing. This result is supported by the improving significance of
the VAR estimation of Japan and Korea, measured by Schwartz-Bayes Information Criterion.
(3) Lack of common results
From the results of our study, we could not find common relationship in all countries
between monetary variables and real economic growth rate, and rate of inflation. On the
contrary, we find apparent differences between countries.
The most striking finding was that the results of Japan and Korea were quite
different. It has been widely believed that economic development of East Asian countries has
a common character, and sound macroeconomic management was considered as one element
of high and stable economic growth. However, as far as this empirical study is concerned, the
results of the monetary policies had completely different conclusions. In the case of Japan,
the importance of the role of interest rate on the economic growth is apparent. But in the case
of Korea, we recognized the strong independent feature of money supply and interest rate.
In a country like Indonesia, where the economic performance is strongly affected by
an exogenous factor like oil, we found the dominance of oil price in determining the economic
feature. Even, though this is a naturally expected result, we found it important to confirm it
by the VAR model. As the relative importance of oil declines, monetary variables started to
influence other economic variables.
In the case of the Philippines, results from the VAR Model were quite different from
what we have expected. However, these results suggest us that confusion of the monetary
from the traditional viewpoint might be one element of the economic turmoil in the
Philippines.
(4) Strong relationship between inflation and interest rate
In the second period of Japan, second period of Korea, and in the Philippines, we
recognized strong causality from inflation on the interest rate. One candidate explanation of
this relationship between the two variables can be considered as the existence of Fisher
Effect in the 1980’s. In the case of Japan and Korea, the exchange rate might be the factor
- 131 -
deriving this result, but it is difficult to deny the existence of inflationary expectations.
- 132 -
Percentage of Variance Decomposition of Forecast Error
Table 4-1 Japan: 1962 Q3 - 1973 Q1
JMR
JMR
JM2
89.49
JY
JPW
3.79
1.23
JY
5.49
→75.35
→12.58
→0.71
1.03
92.06
1.86
5.05
→91.52
→1.85
→5.52
→1.12
JPW
JM2
→11.36
0.75
8.56
90.67
0.02
→2.10
→8.63
→88.81
→0.47
5.98
5.08
1.86
87.08
→5.61
→1.85
→82.29
→10.25
Table 4-2 Japan: 1973 Q2 - 1990 Q1
JMR
JMR
JM2
JPW
JY
JM2
JPW
92.48
0.01
→65.25
→5.11
6.92
→25.83
JY
0.58
→3.82
7.34
84.66
1.70
6.30
→7.15
→82.37
→3.06
→6.42
17.18
10.13
68.31
4.38
→21.43
→12.62
→60.06
→5.89
7.26
0.25
14.08
78.41
→0.95
→15.69
→73.14
→10.22
- 133 -
Table 4-3 Japan: 1973 Q2 - 1990 Q1
JMR
JMR
JM2
JEX
JY
JM2
JEX
JY
86.64
0.75
12.59
0.02
→55.71
→5.54
→38.53
→0.22
2.61
94.23
0.01
3.15
→2.63
→94.10
→0.12
→3.16
2.14
1.72
96.05
0.09
→2.38
→2.59
→94.80
→0.23
12.56
0.05
0.01
87.38
→15.51
→0.52
→3.29
→80.69
JMR: Call Rate
JM2:
M2+CD
JPW:
Wholesale Price Index
JY:
GDP at 1985 Prices
JEX:
Nominal Exchange Rate (Yen/$US)
(note) The figures in the upper row are the percentage of variance in the 2nd period, and
figures in the lower row are those after 20th periods.
Table 4-4 Korea: 1970 Q3 to 1979 Q4
KRDR
KRDR
KRM2
KRPW
KRY
KRM2
KRPW
KRY
98.54
1.13
0.24
0.09
→94.29
→3.32
2.20
→0.18
1.37
93.87
4.14
0.61
→1.58
→93.31
4.36
→0.75
0.34
7.79
90.34
1.53
→1.98
→9.47
87.05
→1.49
1.20
12.78
2.94
83.07
→1.24
→12.71
4.02
→82.03
- 134 -
Table 4-5 Korea: 1980 Q2 to 1990 Q1
KRMR
KRMR
KRM2
KRP
KRM2
KRPW
95.45
2.13
2.28
→85.54
→2.34
0.14
→0.16
3.46
94.07
2.27
0.19
→5.13
→92.13
→2.45
→0.29
4.76
→11.35
KRY
→11.96
KRY
5.24
89.71
0.28
→4.69
→83.69
→0.27
2.72
3.56
1.98
91.74
→3.48
→4.38
→2.63
→89.50
Table 4-6 Korea: 1980 Q2 to 1990 Q1
KRMR
KRMR
KRM2
KREX
KRY
KRM2
KREX
KRY
97.36
2.57
0.05
0.12
→96.09
→3.47
→0.42
→0.00
6.63
91.77
1.43
0.14
→8.49
→89.91
→1.42
→0.17
17.21
0.09
82.52
0.16
→26.32
→0.59
→72.96
→0.16
3.10
3.38
8.35
85.16
→4.57
→3.97
→8.44
→83.00
- 135 -
Table 4-7 Korea: 1980 Q2 to 1990 Q1
KRDR
KRDR
KRM2
KRPW
KRY
KRM2
KRPW
KRY
96.47
0.34
3.18
0.02
→90.70
→0.41
→8.88
→0.01
11.73
82.71
5.56
0.00
→13.24
→81.12
→5.63
→0.01
16.31
5.98
77.68
0.03
→28.55
→4.91
→66.51
→0.03
12.10
1.53
4.56
81.81
→12.73
→1.90
→5.28
→80.08
KRDR: Discount Rate
KRMR: Money Market Rate
KRM2: M2
KRPW: Wholesale Price Index
KREX: Nominal Exchange Rate (Won/$US)
KRY:
GDP at 1985 Prices
Table 4-8 Indonesia: 1977 Q1 to 1983 Q2
IDPR
IDPR
IM2
IPW
IY7
IM2
IPW
IY7
98.22
0.19
1.47
0.12
→94.83
→0.34
→2.52
→2.31
5.16
92.97
0.60
1.27
→4.83
→82.51
→6.83
→5.82
5.66
17.25
77.08
0.01
→6.50
→20.32
→72.64
→0.54
3.73
2.36
17.05
76.86
→4.87
→8.52
→33.85
→52.75
- 136 -
Table 4-9 Indonesia: 1977 Q1 to 1983 Q2
(Oil Price)
IMR
IMR
IM2
OP
IY7
IM2
OP
IY7
99.86
0.05
0.00
0.09
→96.45
→0.09
→2.84
→0.62
8.50
0.15
4.63
86.72
→3.74
→68.76
→27.27
→0.23
1.76
0.20
98.01
0.02
→1.55
→0.17
→98.01
→0.26
1.67
0.15
46.81
51.37
→4.85
→0.06
→74.47
→20.62
Table 4-10 Indonesia: 1984 Q3 to 1989 Q2
IMR
IMR
IM2
IPW
IY8
IM2
IPW
IY8
93.33
5.07
1.59
0.01
→89.34
→4.63
→5.33
→0.70
0.63
83.24
15.78
0.35
→3.44
→72.96
→16.69
→6.92
8.12
29.55
51.78
10.55
→7.39
→31.07
→50.88
→10.65
9.74
8.12
53.97
→8.96
→50.49
28.17
→26.27
→14.27
- 137 -
Table 4-11 Indonesia: 1984 Q3 to 1989 Q2
(Oil Price)
IMR
IMR
IM2
OP
IY8
IM2
IY8
91.72
7.74
0.48
0.07
→91.84
→6.77
→1.31
→0.07
8.46
74.29
17.22
0.03
→8.81
→73.28
→17.51
→0.40
14.38
19.19
65.12
1.31
→21.99
→18.96
→57.85
→1.20
13.77
→13.75
IMR:
IPW:
IM2:
OP:
IY7:
IY8:
OP
9.32
→19.34
26.85
50.06
→24.46
→42.45
Money Market Rate
Wholesale Price Index
M2
Oil Price
GDP at 1973 Prices
GDP at 1983 Prices
Table 4-12 Philippines: 1982 Q3 to 1990 Q1
PHTBR
PHTBR
PHM2
PHPW
90.19
0.13
7.59
→37.57
→20.06
→41.06
PM2
PHPW
PHY
PHTBR:
PHM2:
PHP:
PHY:
PHY
2.09
→1.31
0.58
98.98
0.38
0.06
→1.88
→97.02
→0.98
→0.12
7.18
53.88
38.18
0.76
→9.16
→58.30
→31.92
→0.62
2.50
11.36
28.28
57.87
→2.84
→13.38
→29.62
→54.16
Treasury Bond Rate
M2
Wholesale Price Index
GNP at 1985 Prices
- 138 -
1. Japan
Vector Auto Regression
**********************
(1) Equation 4-1
Current sample: 1962:3 to 1973:1
Number of observations: 43
Schwarz Bayes. Info. Crit. = -26.8044
Variable
JMREQ. JMR (-1)
JM2 (-1)
JPW (-1)
JY (-1)
C
JM2EQ. JMR (-1)
JM2(-1)
JPW (-1)
JY (-1)
C
JPWEQ. JMR (-1)
JM2 (-1)
JPW (-1)
JY (-1)
C
JPYEQ. JMR (-1)
JM2 (-1)
JPW (-1)
JY (-1)
C
Log of likelihood function = 369.848
Estimated
Coefficient
.900127
-12.0822
-8.36899
21.0148
.804815
-.109361E-03
.280703
-.232457
-.259612
.042228
.129800E-02
.259947
-.244403
.012519
-.393954E-02
-.217886E-02
-.118461
.045889
.012616
.043349
Standard
Error
.064154
8.76339
11.4180
9.30956
.760451
.113376E-02
.154871
.201784
.164523
.013439
.897450E-03
.122591
.159726
.130231
.010638
.113251E-02
.154700
.201561
.164341
.013424
Dependent variable: JMR
Std. error of regression = .665884
Durbin-Watson statistic = 1.13886
F-stat. (block exogeneity) = 3.69151
Dependent variable: JM2
Std. error of regression = .011768
Durbin-Watson statistic = 1.91509
F-stat. (block exogeneity) = 1.07589
Dependent variable: JPW
Std. error of regression = .931503E-02
Durbin-Watson statistic = 2.18034
F-stat. (block exogeneity) = 1.93882
Dependent variable: JY
Std. error of regression = .011755
Durbin-Watson statistic = 2.02479
F-stat. (block exogeneity) = 1.28387
- 139 -
t-statistic
14.0307
-1.37872
-.732966
2.25734
1.05834
-.096458
1.81250
-1.15201
-1.57797
3.14222
1.44632
2.12044
-1.53015
.096129
-.370330
-1.92392
-.765746
.227667
.076768
3.22916
(2) Equation 4-2
Current sample: 1973:2 to 1990:1
Number of observations: 68
Schwarz Bayes. Info. Crit. = -28.1055
Variable
JMREQ. JMR (-1)
JM2 (-1)
JPW (-1)
JY (-1)
C
JM2EQ. JMR (-1)
JM2 (-1)
JPW (-1)
JY (-1)
C
JPWEQ. JMR (-1)
JM2 (-1)
JPW (-1)
JY (-1)
C
JPYEQ. JMR (-1)
JM2 (-1)
JPW (-1)
JY (-1)
C
Log of likelihood function = 611.831
Estimated
Coefficient
.794903
-10.2615
39.4336
14.0934
.979189
-.470657E-03
.117429
.238106
.349625
.018615
.109012E-02
.180985
.637587
.396426
-.011271
-.624704E-03
.058510
-.127971
-.237564
.016539
Standard
Error
.054958
12.1704
10.0942
14.1824
.497560
.572650E-03
.126812
.105178
.147776
.518443E-02
.695514E-03
.154020
.127744
.179482
.629677E-02
.492558E-03
.109076
.090468
.127108
.445933E-02
Dependent variable: JMR
Std. error of regression = .866044
Durbin-Watson statistic = 1.32891
F-stat. (block exogeneity) = 5.52845
Dependent variable: JM2
Std. error of regression = .902392E-02
Durbin-Watson statistic = 2.13765
F-stat. (block exogeneity) = 2.68199
Dependent variable: JPW
Std. error of regression = .010960
Durbin-Watson statistic = 2.25898
F-stat. (block exogeneity) = 2.88294
Dependent variable: JY
Std. error of regression = .776182E-02
Durbin-Watson statistic = 2.04453
F-stat. (block exogeneity) = 3.12078
- 140 -
t-statistic
14.4637
-.843152
3.90657
.993724
1.96798
-.821894
.926007
2.26384
2.36591
3.59049
1.56737
1.17507
4.99111
2.20872
-1.78999
-1.26828
.536415
-1.41455
-1.86899
3.70895
(3) Equation 4-3
Current sample: 1973:2 to 1990:1
Number of observations: 68
Schwarz Bayes. Info. Crit. = -25.3029
Variable
JMREQ. JMR (-1)
JM2 (-1)
JEX (-1)
JY (-1)
C
JM2EQ. JMR (-1)
JM2 (-1)
JEX (-1)
JY (-1)
C
JEXEQ. JMR (-1)
JM2 (-1)
JEX (-1)
JY (-1)
C
JPYEQ. JMR (-1)
JM2 (-1)
JEX (-1)
JY (-1)
C
Log of likelihood function = 516.542
Estimated
Coefficient
.908208
13.6799
12.6329
-2.67720
.415205
.323670E-03
.252442
.197265E-02
.237595
.014242
-.624733E-03
.594092
.423381
.154592
-.015821
-.105565E-02
-.013704
.165666E-02
-.176959
.018925
Standard
Error
.041915
10.3511
2.72781
12.8667
.445497
.471752E-03
.116501
.030701
.144814
.501405E-02
.179531E-02
.443358
.116837
.551105
.019082
.396360E-03
.097883
.025795
.121671
.421273E-02
Dependent variable: JMR
Std. error of regression = .833719
Durbin-Watson statistic = 1.38660
F-stat. (block exogeneity) = 7.62543
Dependent variable: JM2
Std. error of regression = .938347E-02
Durbin-Watson statistic = 2.22060
F-stat. (block exogeneity) = .901855
Dependent variable: JEX
Std. error of regression = .035710
Durbin-Watson statistic = 2.03020
F-stat. (block exogeneity) = .742990
Dependent variable: JY
Std. error of regression = .788387E-02
Durbin-Watson statistic = 1.98637
F-stat. (block exogeneity) = 2.37979
- 141 -
t-statistic
21.6678
1.32159
4.63115
-.208072
.932004
.686102
2.16686
.064253
1.64069
2.84036
-.347981
1.33998
3.62368
.280513
-.829149
-2.66336
-.140006
.064225
-1.45441
4.49235
2. Korea
(1) Equation 4-4
Current sample: 1970:3 to 1979:4
Number of observations: 38
Schwarz Bayes. Info. Crit. = -20.2441
Variable
KRDREQ. KRDR (-1)
KRM2 (-1)
KRP (-1)
KRY (-1)
C
KRM2EQ. KRDR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
KRPWEQ. KRDR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
KRYEQ. KRDR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
Log of likelihood function = 205.336
Estimated
Coefficient
.846657
4.30461
-1.92746
1.37595
1.71770
-.148907E-02
-.049798
-.163572
-.079183
.101572
-.260695E-02
-.211136
.367667
-.167003
.079459
-.371686E-03
.370827
-.124433
-.110175
.815030E-02
Standard
Error
.057961
6.31179
4.42501
5.47261
1.06626
.165630E-02
.180368
.126451
.156387
.030470
.207090E-02
.225516
.158103
.195533
.038097
.170863E-02
.186067
.130446
.161329
.031433
Dependent variable: KRDR
Std. error of regression = .917345
Durbin-Watson statistic = 2.12080
F-stat. (block exogeneity) = .334007
Dependent variable: KRM2
Std. error of regression = .026214
Durbin-Watson statistic = 1.98782
F-stat. (block exogeneity) = .723957
Dependent variable: KRPW
Std. error of regression = .032776
Durbin-Watson statistic = 1.69365
F-stat. (block exogeneity) = 1.03627
Dependent variable: KRY
Std. error of regression = .027043
Durbin-Watson statistic = 2.22318
F-stat. (block exogeneity) = 2.04187
- 142 -
t-statistic
14.6075
.681996
-.435584
.251424
1.61095
-.899033
-.276090
-1.29356
-.506323
3.33351
-1.25885
-.936233
2.32549
-.854090
2.08572
-.217534
1.99298
-.953905
-.682925
.259294
(2) Equation 4-5
Current sample: 1980:2 to 1990:1
Number of observations: 40
Schwarz Bayes. Info. Crit. = -22.2516
Variable
KRMREQ. KRMR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
KRM2EQ. KRMR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
KRPWEQ. KRMR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
KRYEQ. KRMR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
Log of likelihood function = 254.890
Estimated
Coefficient
.824025
-10.0336
15.4276
2.47105
2.19152
.363247E-02
-.151215
-.270265
.056117
.686310E-02
.178127E-02
-.107616
.438884
.040705
-.014848
-.233900E-02
.171535
-.101731
-.414768
.055217
Standard
Error
.060461
6.50674
8.71647
7.22165
.789869
.152208E-02
.163805
.219435
.181803
.019885
.874374E-03
.094100
.126056
.104438
.011423
.127683E-02
.137411
.184077
.152509
.016681
Dependent variable: KRMR
Std. error of regression = 1.01419
Durbin-Watson statistic = 1.85055
F-stat. (block exogeneity) = 1.71189
Dependent variable: KRM2
Std. error of regression = 0.025532
Durbin-Watson statistic = 1.96943
F-stat. (block exogeneity) = 2.11143
Dependent variable: KRPW
Std. error of regression = .014667
Durbin-Watson statistic = 2.24643
F-stat. (block exogeneity) = 1.72352
Dependent variable: KRY
Std. error of regression = .021418
Durbin-Watson statistic = 2.15658
F-stat. (block exogeneity) = 3.81968
- 143 -
t-statistic
13.6291
-1.54204
1.76994
.342173
2.77454
2.38652
-.923139
-1.23164
.308672
.345145
2.03719
-1.14364
3.48165
.389753
-1.29986
-1.83189
1.24833
-.552656
-2.71963
3.31023
(3) Equation 4-6
Current sample: 1980:2 to 1990:1
Number of observations: 40
Schwarz Bayes. Info. Crit. = -21.9709
Variable
KRMREQ. KRMR (-1)
KRM2 (-1)
KREX (-1)
KY (-1)
C
KRM2EQ. KRMR (-1)
KRM2 (-1)
KREX (-1)
KRY (-1)
C
KREXEQ. KRMR (-1)
KRM2 (-1)
KREX (-1)
KRY (-1)
C
KRY. KRMR (-1)
KRM2 (-1)
KREX (-1)
KRY (-1)
C
Log of likelihood function = 249.276
Estimated
Coefficient
.891188
-9.20701
2.34348
.894441
1.50486
.313531E-02
-.158983
-.179059
.049661
.011673
.257228E-02
.019203
.189781
-.036233
-.030310
-.291331E-02
.164785
.011243
-.397780
.061141
Standard
Error
.052662
6.76240
6.26709
7.57492
.746764
.127482E-02
.163702
.151712
.183372
.018077
.883319E-03
.113429
.105121
.127058
.012526
.107212E-02
.137673
.127589
.154215
.015203
Dependent variable: KRMR
Std. error of regression = 1.05650
Durbin-Watson statistic = 1.73247
F-stat. (block exogeneity) = .661877
Dependent variable: KRM2
Std. error of regression = 0.25575
Durbin-Watson statistic = 2.11432
F-stat. (block exogeneity) = 2.06467
Dependent variable: KREX
Std. error of regression = .017721
Durbin-Watson statistic = 1.09011
F-stat. (block exogeneity) = 3.15715
Dependent variable: KRY
Std. error of regression = .021509
Durbin-Watson statistic = 2.10850
F-stat. (block exogeneity) = 3.68911
- 144 -
t-statistic
16.9229
-1.36150
.373935
.118079
2.01518
2.45941
-.971173
-1.18026
.270823
.645710
2.91206
.169296
1.80536
-.285173
-2.41978
-2.71733
1.19693
.088115
-2.57939
4.02165
(4) Equation 4-7
Current sample: 1980:2 to 1990:1
Number of observations: 40
Schwarz Bayes. Info. Crit. = -22.3340
Variable
KRDREQ. KRDR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
KRM2EQ. KRDR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
KRPWEQ. KRDR (-1)
KRM2 (-1)
KRP (-1)
KRY (-1)
C
KRYEQ. KRDR (-1)
KRM2 (-1)
KRPW (-1)
KRY (-1)
C
Log of likelihood function = 256.538
Estimated
Coefficient
.742031
-7.01070
21.2778
-1.00168
1.84048
.456822E-02
-.094609
-.483496
.374868E-02
.017759
.281105E-02
-.077119
.259540
.013645
-.013306
-.234201E-02
.137961
-.042958
-.382496
.044210
Standard
Error
.080512
7.51658
12.5703
8.32867
.700066
.172174E-02
.160742
.268814
.178108
.014971
.951122E-03
.088797
.148498
.098390
.827019E-02
.148487E-02
.138628
.231833
.153605
.012911
Dependent variable: KRDR
Std. error of regression = 1.17468
Durbin-Watson statistic = 1.23997
F-stat. (block exogeneity) = 1.15813
Dependent variable: KRM2
Std. error of regression = .025120
Durbin-Watson statistic = 2.10426
F-stat. (block exogeneity) = 2.56656
Dependent variable: KRPW
Std. error of regression = .013877
Durbin-Watson statistic = 2.19854
F-stat. (block exogeneity) = 3.29165
Dependent variable: KRY
Std. error of regression = .021665
Durbin-Watson statistic = 2.15388
F-stat. (block exogeneity) = 3.46917
- 145 -
t-statistic
9.21642
-.932698
1.69270
-.120269
2.62901
2.65325
-.588576
-1.79862
.021047
1.18626
2.95551
-.868490
1.74776
.138678
-1.60885
-1.57725
.995192
-.185296
-2.49013
3.42411
3. Indonesia
(1) Equation 4-8
Current sample: 1977:1 to 1983:2
Number of observations: 26
Schwarz Bayes. Info. Crit. = -26.6994
Variable
IMREQ. IDPR (-1)
IM2 (-1)
IPW (-1)
IY7 (-1)
C
IM2EQ. IMR (-1)
IM2 (-1)
IPW (-1)
IY7 (-1)
C
IPWEQ. IR (-1)
IM2 (-1)
IP (-1)
IY (-1)
C
IY7EQ. IR (-1)
IM2 (-1)
IP (-1)
IY (-1)
C
Log of likelihood function = 232.103
Estimated
Coefficient
.605647
1.76444
-6.02154
8.13289
2.34302
-.508736E-02
.264759
.136436
1.13748
.056839
-.166045E-02
.171161
.322149
-.042433
.021718
.383469E-03
-.023664
.107372
.852997
-.224436E-02
Standard
Error
.088093
4.73371
7.61341
19.0877
.678632
.412388E-02
.221599
.356406
.893551
.031769
.226119E-02
.121507
.195424
.489950
.017419
.445924E-03
.023962
.038539
.096622
.343523E-02
Dependent variable: IDPR
Std. error of regression = .599388
Durbin-Watson statistic = 1.96636
F-stat. (block exogeneity) = .282292
Dependent variable: IM2
Std. error of regression = .028059
Durbin-Watson statistic = 1.89168
F-stat. (block exogeneity) = .820767
Dependent variable: IPW
Std. error of regression = .015385
Durbin-Watson statistic = 1.80635
F-stat. (block exogeneity) = 1.21010
Dependent variable: IY7
Std. error of regression = .303409E-02
Durbin-Watson statistic = .862584
F-stat. (block exogeneity) = 2.81794
- 146 -
t-statistic
6.87510
.372738
-.790912
.426080
3.45257
-1.23364
1.19477
.382811
1.27299
1.78916
-.734323
1.40866
1.64847
-.086607
1.24675
.859943
-.987553
2.78606
8.82822
-.653338
(2) Equation 4-9
Current sample: 1977:1 to 1983:2
Number of observations: 26
Schwarz Bayes. Info. Crit. = -23.9443
Variable
IMREQ. IMR (-1)
IM2 (-1)
OP (-1)
IY7 (-1)
C
IM2EQ. IMR (-1)
IM2 (-1)
OP (-1)
IY7 (-1)
C
OPEQ. IMR (-1)
IM2 (-1)
OP (-1)
IY7 (-1)
C
YEQ. IMR (-1)
IM2 (-1)
OP (-1)
IY7 (-1)
C
Log of likelihood function = 196.288
Estimated
Coefficient
.606649
.587406
-.136011
7.94720
2.23677
-.339274E-02
.249014
.118231
.428893
.057773
-.167341E-02
-.026250
.753893
-.454072
.027315
.688816E-03
-.010659
.024098
.722153
-.627161E-03
Standard
Error
.093168
4.58492
1.78169
22.2484
.674874
.409707E-02
.201623
.078350
.978376
.029678
.010220
.502951
.195446
2.44057
.074031
.469350E-03
.023097
.897562E-02
.112080
.339981E-02
Dependent variable: IMR
Std. error of regression = .608165
Durbin-Watson statistic = 1.99501
F-stat. (block exogeneity) = .073606
Dependent variable: IM2
Std. error of regression = .026744
Durbin-Watson statistic = 2.10234
F-stat. (block exogeneity) = 1.60873
Dependent variable: OP
Std. error of regression = .066714
Durbin-Watson statistic = 1.83010
F-stat. (block exogeneity) = .040008
Dependent variable: IY7
Std. error of regression = .306375E-02
Durbin-Watson statistic = 1.21810
F-stat. (block exogeneity) = 2.62880
- 147 -
t-statistic
6.51136
.128117
-.076338
.357203
3.31435
-.828090
1.23505
1.50901
.438372
1.94668
-.163735
-.052191
3.85730
-.186052
.368961
1.46760
-.461464
2.68477
6.44317
-.184470
(3) Equation 4-10
Current sample: 1984:3 to 1989:2
Number of observations: 20
Schwarz Bayes. Info. Crit. = -22.6459
Variable
IMREQ. IMR (-1)
IM2 (-1)
IPW (-1)
IY8 (-1)
C
IM2EQ. IMR (-1)
IM2 (-1)
IPW (-1)
IY8 (-1)
C
IPWEQ. IMR (-1)
IM2 (-1)
IPW (-1)
IY8 (-1)
C
IY8EQ. IMR (-1)
IM2 (-1)
IPW (-1)
IY8 (-1)
C
Log of likelihood function = 142.901
Estimated
Coefficient
.651790
-21.9805
39.8632
-1.92016
5.04099
.935075E-03
-.510126
-1.75363
.153587
.098936
.211221E-03
.153808
.085061
.272763
.447389E-03
-.139596E-03
.043950
-.599399
-.337155
.025575
Standard
Error
.172158
14.1031
41.9167
24.9438
2.49844
.241283E-02
.197659
.587472
.349593
.035016
.722062E-03
.059151
.175806
.104619
.010479
.150052E-02
.122922
.365343
.217409
.021776
Dependent variable: IMR
Std. error of regression = 1.88437
Durbin-Watson statistic = 1.98230
F-stat. (block exogeneity) = 1.29380
Dependent variable: IM2
Std. error of regression = 0.264100
Durbin-Watson statistic = 1.57479
F-stat. (block exogeneity) = 3.09996
Dependent variable: IPW
Std. error of regression = .790339E-02
Durbin-Watson statistic = 1.60981
F-stat. (block exogeneity) = 5.90256
Dependent variable: IY8
Std. error of regression = .016424
Durbin-Watson statistic = 1.60020
F-stat. (block exogeneity) = .995128
- 148 -
t-statistic
3.78600
-1.55855
.951010
-.076979
2.01765
.387542
-2.58084
-2.98505
.439331
2.82544
.292525
2.60025
.483832
2.60721
.042694
-.093032
.357547
-1.64065
-1.55079
1.17443
(4) Equation 4-11
Current sample: 1984:3 to 1989:2
Number of observations: 20
Schwarz Bayes. Info. Crit. = -17.2995
Variable
IMREQ. IMR (-1)
IM2 (-1)
OP (-1)
IY8 (-1)
C
IM2EQ. IMR (-1)
IM2 (-1)
OP (-1)
IY8 (-1)
C
IPWEQ. IMR (-1)
IM2 (-1)
OP (-1)
IY8 (-1)
C
IY8EQ. IMR (-1)
IM2 (-1)
OP (-1)
IY8 (-1)
C
Log of likelihood function = 89.4374
Estimated
Coefficient
.697872
-23.0223
-1.58770
-5.96290
5.17178
.360406E-02
-.439590
-.125781
-.060170
.030455
.015921
1.25823
.381013
1.65146
-.303870
.170574E-02
.072968
-.081860
-.488023
-.010295
Standard
Error
.194693
14.3671
3.48106
26.4268
2.70195
.283719E-02
.209367
.050728
.385109
.039374
.013780
1.01686
.246378
1.87041
.191235
.136021E-02
.100375
.024320
.184629
.018877
Dependent variable: IMR
Std. error of regression = 1.92703
Durbin-Watson statistic = 1.73677
F-stat. (block exogeneity) = 1.01822
Dependent variable: IM2
Std. error of regression = .028082
Durbin-Watson statistic = 1.47075
F-stat. (block exogeneity) = 2.16411
Dependent variable: OP
Std. error of regression = .136389
Durbin-Watson statistic = 1.60857
F-stat. (block exogeneity) = 1.62594
Dependent variable: IY8
Std. error of regression = .013463
Durbin-Watson statistic = 1.69666
F-stat. (block exogeneity) = 3.92222
- 149 -
t-statistic
3.58447
-1.60243
-.456098
-.225638
1.91409
1.27029
-2.09962
-2.47951
-.156241
.773482
1.15538
1.23737
1.54645
.882940
-1.58898
1.25403
.726953
-3.36595
-2.64326
-.545389
4. Philippines
Equation 4-12
Current sample: 1982:3 to 1990:1
Number of observations: 31
Schwarz Bayes. Info. Crit. = -16.7562
Variable
PHTBREQ. PHTBR (-1)
PHM2 (-1)
PHPW (-1)
PHY (-1)
C
PHM2EQ. PHTBR (-1)
PHM2 (-1)
PHPW (-1)
PHY (-1)
C
PHPWEQ. PHTBR (-1)
PHM2 (-1)
PHPW (-1)
PHY (-1)
C
PHY PHTBR (-1)
PHM2 (-1)
PHPW (-1)
PHY (-1)
C
Log of likelihood function = 118.112
Estimated
Coefficient
.646850
-10.9542
92.9516
33.1530
3.82373
-.198634E-02
-.026452
.290922
.082010
.075610
-.748290E-03
.210343
.694169
.153360
.012281
-.116535E-02
-.090198
-.139237
-.517944
.037696
Standard
Error
.093431
10.4150
19.5523
23.6973
1.87460
.197063E-02
.219670
.412392
.499818
.039539
.708785E-03
.079010
.148327
.179772
.014221
.769808E-03
.085812
.161097
.195249
.015445
Dependent variable: PHTBR
Std. error of regression = 3.46935
Durbin-Watson statistic = 2.71810
F-stat. (block exogeneity) = 7.76583
Dependent variable: PHM2
Std. error of regression = 0.73175
Durbin-Watson statistic = 2.03801
F-stat. (block exogeneity) = 0.389856
Dependent variable: PHPW
Std. error of regression = .026319
Durbin-Watson statistic = 2.53734
F-stat. (block exogeneity) = 3.78425
Dependent variable: PHY
Std. error of regression = .028585
Durbin-Watson statistic = 2.11782
F-stat. (block exogeneity) = 2.02899
- 150 -
t-statistic
6.92326
-1.05177
4.75401
1.39902
2.03975
-1.00797
-.120417
.705450
.164080
1.91230
-1.05574
2.66225
4.68000
.853084
.863563
-1.51382
-1.05111
-.864306
-2.65274
2.44057
Definition of Variables Used in VAR model in Chapter4
Japan:
JPY:
GNP at 1985 Price (Billion Yen)
JPW:
Wholesale Price Index (1985=100)
JPC:
Consumer Price Index (1985=100)
JDEF:
GNP Deflator at 1985 Prices
JBP:
Current Account Balance (million $US)
JR:
Foreign Reserves (million $US)
JIIP:
Industrial Production Index (1985=100)
JEX:
Exchange Rate (Yen/$US)
JM1:
Money (Billion Yen)
JM2:
Money+Quasi-money+CD (Billion Yen)
JMR:
Call Rate (%)
JDR:
Discount Rate (%)
JIIP:
Industrial Production Index (1985=100)
Korea:
KRY:
GNP at 1985 Price (Billion Won)
KRPW:
Wholesale Price Index (1985=100)
KRPC:
Consumer Price Index (1985=100)
KRDEF:
GNP Deflator at 1985 Prices
KRBP:
Current Account Balance (million $US)
KRR:
Foreign Reserves (million $US)
KRIIP:
Industrial Production Index (1985=100)
KREX:
Exchange Rate (Won/$US)
KRM1:
Money (Billion won)
KRM2:
Money+Quasi-money (Billion won)
KRDPR:
Deposit Rate (%)
KRDR:
Discount Rate (%)
KRMR:
Money Market Rate (%)
KRIIP:
Industrial Production Index (1985=100)
- 151 -
Indonesia:
IY7:
GDP at 1973 Price (Billion Rupia)
IY8:
GDP at 1983 Price (Billion Rupia)
IPW:
Wholesale Price Index (1985=100)
IPC:
Consumer Price Index (1985=100)
IDEF7:
GDP Deflator at 1973 Price
IDEF8:
GDP Deflator at 1983 Price
IBP:
Current Account Balance (million $US)
IR:
Foreign Reserves (million $US)
IIIP:
Industrial Production Index (1985=100)
IEX:
Exchange Rate (Rupia/$US)
IM1:
Money (Billion Rupias)
IM2:
Money+Quasi-money (Billion Rupias)
IMR:
Money Market Rate (%)
IDPR:
Deposit Rate (%)
OP:
Oil Price ($/bbl)
The Philippines:
PHY:
GNP at 1985 Price (Billion Pesos)
PHPW:
Wholesale Price Index (1985=100)
PHPC:
Consumer Price Index (1985=100)
PHDEF:
GNP Deflator at 1985 Prices
PHBP:
Current Account Balance (million $US)
PHR:
Foreign Reserves (million $US)
PHIIP:
Industrial Production Index (1985=100)
PHEX:
Exchange Rate (Peso/$US)
PHM1:
Money (Billion Pesos)
PHM2:
Money+Quasi-money (Billion Pesos)
PHTBR:
Treasury Bill Rate (%)
PHLR:
Bank Lending Rate (Average %)
Note:
(1) (-1) indicates for one period lagged variables, and (-2)
indicates for two periods lagged variables.
- 152 -
Conclusion - Targets, Effectiveness and Results of Monetary Policy
In the previous chapters, we have studied the monetary policies of four Asian
countries by applying reaction function, money demand function, and VAR, to determine
policy targets and results.
In interpreting the results, we must recognize that these analyses do not have direct
relationships to each other, and they are under different assumptions, and they employ
different partial equilibrium analysis. In spite of these features, in this chapter, we will
attempt to evaluate the monetary policy of four countries from the conclusions of each study
by following Table 5-1, in which all the results are summarized.
5.1 Summary of individual countries
(1) Japan
In the fixed exchange rate period (1962Q1 to 1973Q1), targets of the monetary policy
of Japan, shown by reaction (response) functions on discount rate, were real GNP growth,
growth of industrial production and current account balance. On the reaction function of the
money supply, real GNP growth, growth of industrial production and exchange rate were
significant. When we applied lagged macroeconomic variables in the response function on
discount rate and money supply, we have confirmed that the monetary policy in the first
period rather responded to the contemporaneous information. The result of the response
function with the rolling regression method show that relevance of monetary policy explicate
considerable variability in the first period. We can interpret that monetary policy was
executed in line with the well-known principle of “Stop-and-Go.”
The significance level of money demand function on both interest rate and money
supply, for the first period was 10%, and the result of sequential Chow test shows an
apparent structural break in early 1970’s. In spite of these questions, we can generally accept
that the estimations of money demand function in the first period show significant results.
VAR analyses show strong mutual relations between interest rate and real GNP
growth which indicate “fine tuning” policy by using interest rate as a policy tool. However,
relationship between money supply and interest rate was not so strong, and this result shows
that other variables had stronger impact on money supply or interest rate than the mutual
effect between these two monetary variables.
We can summarize the monetary policy of Japan in the first period that monetary
authorities focused on adjusting current account balance and the real economic growth, while
money demand function shows a background not so strong but acceptable for the
implementation of monetary policy. These results confirm that “fine tuning” type of
- 153 -
monetary policy had been implemented.
In the second period, which is the flexible exchange rate period from 1973Q1 to
1990Q1, the results of the reaction function on discount rate show that the monetary
authority’s major concern was containing inflation. Also, from the reaction function on money
supply, we can evaluate, with a lower confidence, that the concern of the monetary policy was
containing inflation. The application of the lagged variables in the reaction function, showed
that the discount rate responds to the contemporaneous changes of the exchange rate
movements and wholesale prices. On the other hand, the importance of the inflationary
consideration revealed in the second period. Increasing significance of money demand
functions show that the background of the effectiveness of monetary policy is becoming
stronger. However, from the result of the sequential Chow test, we should note that there
instability exists due to the structural break in the early 1970’s.
The results of the monetary policy from VAR estimation still indicate strong effect of
interest rate on real economic growth, but the interest rate itself was strongly influenced by
inflation or exchange rate, indicating that the interest rate was not determined
independently.
We can conclude that, in general, the monetary policy of Japan had clear targets,
had been implemented appropriately and the result is significant.
(2) Korea
In the fixed exchange rate period (1970Q1 to 1979Q4), results of the reaction
function show that discount rate is responding only to the growth of industrial production,
but money supply is responding to inflation rate and current account balance. When we
applied lagged macroeconomic variable, current account balance and GNP deflator for the
discount rate, and consumer price for the money supply responded. From the analysis of the
rolling regressions, current account balance, in the whole part of the first period, and
inflation, in the latter part, showed up as the target of monetary policy. We can interpret and
confirm that there was a Stop-and-Go character of monetary policy in this period. When we
consider the interest rate in Korea was a tool of rationing of the subsidized credits; only the
reaction function on money supply is supposed to demonstrate the targets of the monetary
policy. Money demand function is significant, only for 10% level, both on interest rate and
money supply respectively, which leaves doubt as to the effectiveness of the monetary policy
in Korea. From the result of sequential Chow test, the appearance of the structural break
shown in the late 1974 might been indicating this result. VAR analyses show an impact from
money supply on real GNP growth and inflation, but mutual relations between variables are
weak. We can conclude that Korean monetary authorities intended to stabilize the country’s
macroeconomic situation, especially containing inflation and adjusting current account
- 154 -
balance by using money supply and discount rate as a policy tool. However the result of the
monetary policy does not show significant effectiveness.
In the second period, targets of the monetary policy, demonstrated by the reaction
function on discount rate, can be identified by growth of real GDP and industrial production,
but no variable was significant in reaction function on money supply; a result which remains
as a puzzle in our study. Many explanations can be offered as we have done in Chapter 2.
Applying lagged macroeconomic variables in the response function did not change the result
significantly. Results of the rolling regression is suggesting the change of the relevance of
targets of the monetary policy, such as Stop-and-Go character in 1984, stabilization aspects in
1983, and anti-inflationary aspects in 1988. Significance of money demand function slightly
improved compared to the first period, confirming that the effectiveness of monetary policy
also improved in the second period. As in the analysis of the first period, result of the
sequential Chow test shows a possibility of structural break in 1983. Results of VAR analysis
only show the strong relationship between interest rate and inflation rate. But when we
replace money market by discount rate, the result turns out that money supply affects
interest rate and real GNP growth which suggests the effective result of the monetary policy.
We can summarize that Korean monetary policy approached that of Japan in the
1980’s, even with the existence of strong credit rationing mechanism of the monetary policy.
This result might be indicating that the effect of financial liberalization is gradually
becoming apparent in the 1980’s.
(3) Indonesia
In the case of Indonesia, because the discount window was not used as a policy tool,
we only estimated the reaction function on money supply. In the first period, no variable
showed significance in the reaction function on money supply. However the result of the
response function with the application of lagged macroeconomic variables suggested the
existence of serious time lag problem in Indonesia. From the result of the rolling regression,
stabilization character weakly revealed in 1979 and 1980. Money demand function, even
though there is a sign of structural break in 1982, shows acceptable significance level
informing us of suitable conditions for the monetary policy to be effective. When we
attempted to examine the result of monetary policy by VAR analysis, we found strong
influence of oil price on the entire economy, which made other variables ineffective. Naturally,
monetary policy had limited effect on macroeconomic conditions.
The results of the analysis in the second period show that reaction function became
significant on inflation rate. Particularly this result was confirmed by the application of the
rolling regression method. Money demand function shows an acceptable significance level
- 155 -
Table 5-1 Analysis of Monetary Policy
Country
Period
Japan
1962Q1 - 1973Q1
1973Q2 - 1990Q1
Korea
1970Q1 - 1979Q4
1980Q1 - 1990Q1
- 156 -
Indonesia
Philippines
Reaction Function
Money Demand Function
VAR
Discount rate
JPY, JIIP, JBP
(JDEF@)
Money Supply
JPY, JIIP, JBP, (JR)
Discount rate
(JGNP), JPW, JIIP, JEX
Money Supply
none
(JPW@, JDEF@)
Interest rate 10%
GNP 10%
JPY < - > JMR
JM2 - > JMR
Interest rate 1%
GNP 1%
JM2 - > JWP
JMR - > JPY JPW - > JPY
JMR < - > JPW
Discount rate
KRIIP
Money Supply
KRPW, KRDEF, KRBP
Discount rate
(KRY), (KRIIP)
Money Supply
none
(KRIIP@,KRBP@,KRDEF@)
Interest rate 10%
GNP 1%
KRM2 - > KRY, (KRM2 - > KRPW)
Interest rate 10%
GNP 5%
KRMR < - > KRPW
KRMR - > KRY, KRMR - > KR2M**
KRMR - > KREX*
JEX - > JMR *
JMR - > JPY*
1976Q1 - 1983Q4
Money Supply
None
Interest rate 10%
GNP 1%
IM2 - > IPW, IPW - > IY7
OP - > IY7, OP - > IM2***
1983Q1 - 1990Q1
Money Supply
IPW
Interest rate 5%
GNP 5%
(1983Q3 - 1990Q1)
IMR - > IY8, IM2 - > IY8
IM2 - > IPW IPW - > IM2
1981Q1 - 1990Q1
Discount rate
PHPW, PHPC, PHDEF, PHR, (PHBP)
Money supply
PHY
* Exchange rate (local currency per $US) replacing wholesale price index.
** Discount rate replacing money market rate.
*** Oil price ($/bbl) replacing wholesale price index.
NOTE(1) Variables in parenthesis are results of the estimation which were less significant
(Significance level over 10% in response function and slightly less than 10% contribution
on variance decomposition in VAR)
NOTE(2) Variables in parenthesis with @ are the variables which could be considered
as targets from the rolling regression analysis.
NOTE(3) For the definition of variables, see Chapter 4.
Interest rate X
GNP 10%
OP - > Y, R - > Y, M - > Y***
R - > OP, M - > OP, OP - > M***
PHM2 - > PHY, PHPW - > PHY
PHPW - > PHTBR, PHW2 - > PHPW
with a sign of structural break in 1986 shown by the result of sequential Chow test. The effect
of oil prices became less influential, which was apparent from VAR analysis, and effects of
other variables became stronger in the second period.
We can conclude that in the case of Indonesia, there was the dominant effect of oil,
and we could not find significant targets or results of the monetary policy in the first period.
In the second period, which include late 1980’s, the oil effect diminished but still had an
impact on the Indonesian economy. Financial liberalization seemed to have a positive effect,
when we take into consideration the result of money demand function, and the result of VAR
estimation showing the increasing effect of monetary variable on other economic variables.
(4) The Philippines
The monetary policy of the Philippines provided the most interesting and puzzling
results in our study. Reaction function indicates that the authorities were trying to apply
counter cyclical monetary policy. But the result of the rolling regression showed the puzzling
result do to the fact that most of the coefficients showed significance with opposite signs.
However, the result of the regression with lagged variable showed that the information
problem was not so critical. Money demand function shows insignificant effect on interest
rates. But when we constructed money demand function replacing interest rate by expected
inflation rate, the result became significant. This means that the transmission mechanism of
the money supply is completely different from that of the traditional money demand function.
VAR analysis suggests that the result of the monetary policy was extremely confusing and we
could not obtain satisfactory explanation of the relationship between variables.
5.2 Target, effectiveness and the result of the monetary policy
In the introduction, we raised several questions which will be investigated in this
study. Here, we would like to check whether the answers to these questions were given or not.
“What are the targets of the monetary policy?” We found from the estimation of the
reaction functions that containing inflation was the common target in all countries. In the
limited cases such as Japan in the first period, Korea in the first period, and in the
Philippines, real GNP growth or growth of industrial production, which can be considered as
adjusting business cycle, were targets of monetary policy. We are able to conclude that the
targets of the monetary policy in these countries were used as a tool of stabilization, mainly
by controlling inflation.
“Is there a stable base for the monetary policy to function effectively?” The results of
estimation of the money demand functions of Japan, Korea and Indonesia, generally show
- 157 -
the existence of stable relationship between money demand, interest rate and income.
However, in the case of the Philippines, we obtained a completely insignificant coefficient.
But in an alternative estimation, we found that the expected inflation was significant.
“Did monetary policy had real effects on the macroeconomic conditions?” From the
VAR analysis, we found that the interest rate had significant impacts on real economic
growth in Japan, Indonesia in the second period, and Korea in the second period. Money
supply had impacts on real GNP (GDP) growth in Korea in the first period, Indonesia in the
second period, and in the Philippines.
One interesting finding was that the F-value of the VAR equation in almost all the
variables in the “second periods” of Japan, Korea and Indonesia is increasing. These results
suggest that the relationships between monetary variable and other economic variables are
increasing, which implies that the effect of the monetary policy is getting stronger.
5.3 Economic development and monetary policy
Despite the small sample of countries, we are able to characterize the results by
stages of economic development. When we classify the countries by development stage, Japan
is apparently a developed country, Korea is a representative of fast-growing NIEs, and
Indonesia and the Philippines are developing countries. Concerning the results of the
reaction function of Japan and Korea, we can see clear objectives of the monetary policy.
Money demand functions in both countries show significant results indicating suitable
conditions for effectiveness of the monetary policy. From the VAR analysis, we found that
money supply and/or interest rate had effects on real economic growth.
Indonesia and the Philippines show interesting contrasts. Comparing the economic
development stages of the two countries, represented by per capita GNP and industrial
structure, the Philippines are in a developed stage compared to Indonesia. However,
regarding macroeconomic management, we can see that Indonesia succeeded but the
Philippines apparently failed. There are dangers injumping to that conclusion; but this result
is suggesting that: in a developing country, reasonable monetary policy is one of the key
elements of the stability of the macroeconomic management.
5.4 Remaining issues and possible future study
In this study, we applied the same model regardless of the level of economic
development, exchange rate regime, or restrictions on capital mobility. With this method, we
were able to obtain some common features and differences of the monetary policies.
However, we must point out that there should be differences in the monetary policies when
there are differences in the exchange rate management and restrictions on capital mobility.
- 158 -
We might have obtained different results by formulating an alternative method to explain
monetary policy by using different models in which the differences in the exchange rates and
restrictions in capital mobility could be considered. Furthermore, it is much more desirable to
capture and analyze the interrelationship between monetary and exchange rate policies.1 If
these weak points are conquered, and if this study is expanded to many countries, we might
be able to achieve comparative study of the monetary policies of different countries.
In this study, we applied a simple autoregressive model in estimating money
demand function, and an applied VAR model based on the difference model to examine the
result of monetary policy. However, we can expect more detailed and accurate results in the
estimation of money demand function by examining the existence of cointegration and
applying error correction model. Also, by executing a unit root test, if we apply Structural
VAR by using level data, we will be able to identify the result of the monetary policy from the
sign of the coefficients, and at the same time clearly examine demand shocks and supply
shocks.
In Appendix 2, we attempted a trial study of foreign exchange rate policy with the
application of response function and VAR analysis. We obtained common results that the
target of foreign exchange rate policy were the adjustment of current account. But from the
result of the VAR, exchange rate had stronger effect on economic growth. An interesting
interpretation was shown as a comparison between the analysis of Korea and the Philippines.
The finding of the analysis in Korea showed effective application of the exchange rate policy,
while in the Philippine case it was not. This result might be suggesting close relation between
overall macroeconomic performance and the exchange rate policy.
Furthermore, in this study, we employed three empirical methods, the monetary
reaction function, money demand function, and VAR analysis, rather independently. However
it is obvious that these three empirical analyses should be integrated into simple framework.
A first step in this direction could be to estimate a small model which incorporates the
determination of the money and money demand simultaneously.2
These are tasks remaining for us in order to improve our study, which might enable
more exciting results, and thus provide us with more information on the policy implications.
1
An empirical analysis of exchange rate policies in these countries, using same method in
the main text is shown in appendix.
2
Fry, Lilien and Wadhwa (1988) actually estimated such type of model using cross country
data for the Pacific Basin developing countries.
- 159 -
Even with these limitations, this study showed that the application of time series method on
analyzing developing countries has possibility of deriving fruitful implications.
- 160 -
References
Adekunle, B. (1978). “The Demand for Money: Evidence from Developed and Less Developed
Countries.”, in Coats, W. and Khatkhate, D. eds., Money and Monetary Policy in Developed
Countries, Washington: International Monetary Fund 1978.
Aghevli, Bijan B. (1978). “Effects of Banking Development on the Demand for Money.”, in
Coats and Khatkhate, op. cit.
Aghevli, B., and Khan, M. (1978). “Government Deficits and the Inflationary Process in
Developing Countries.”, in Coats and Khatkhate, op. cit.
Aghevli, B., Khan, M., and Montiel, Peter J. (1991). “Exchange Rate Policy in Developing
Countries: Some Analytical Issues.” International Monetary Fund Occasional Paper 78
(March 1991).
Aghevli, B., Khan M., Narvekar P., and Short B. (1979). “Monetary Policy in Selected Asian
Countries.” International Monetary Fund, Staff Papers Vol. 26 (December 1979).
Ahmad, M. (1993). “Pattern and Effects of Financial Sector Reform in Indonesia.”
Department of Research Cooperation of Economic Research Institute of Economic Planning
Agency, Working Paper No. 23. Japan: (May 1993).
Ahmed, Sadiq., and Kapur Basant K. (1990). “How Indonesia’s Monetary Policy Affects Key
Variables.” Policy, Research, and External Affairs Working Paper Series WPS No. 349, The
World Bank.
Ambler, Steve (1989). “Does Money Matter in Canada? Evidences from a Vector Error
Correction Model.” L’Actualite-Economic Vol. 65, No.4 (December 1989).
Apostol, Agnes B. (1991). “The Money Market in the Philippines in the 1980s.” The Central
Bank of the Philippines CB Review, (March 1991).
Artis, M., and Lewis M. (1984). “How Unstable is the Demand for Money in the United
Kingdom?” Economica, 51.
- 161 -
Asako, K. and S. Kano (1989). “Nihon no Zaiseikinyuu Seisaku no Seikaku Mokuhyou to
Seigyo Kanousei: 1968-1986 (in Japanese: Controllability of Target of Fiscal and Monetary
Policy: 1968 to 1986).” Ookurasyou Zaisei Kinyuu Kenkyuusyo, Financial Review, (May
1989).
Baba, Y. (1995). “Error Correction Money Demand Function for the Japanese Economy (in
Japanese).”, in Honda Y. Eds., On Japanese Business Cycle, Tokyo: Yuuhikaku, 1995.
Balino, T. J. T. (1978). “The Demand for Money and its Components in Argentina: Annual
Estimations 1935-69.”, in Coats, W. and Khatkhate, D. eds., op. cit.
Ban, Kanemi (1991). “Makuro Keiryou Moderu Bunseki (in Japanese: Analysis of
Macro-Econometric Models)”. Yuuhikaku.
Bank of Korea (1990). Banking System in Korea.
Bank
of
Korea,
Research
Department
(1993).
“Financial
Liberalization
and
Internationalization in Korea.” (September 1993).
Bera, A., and Jarque, (1981). “An Efficient Large-Sample Test for Normality of Observations
and Regression Residuals.” Australian National University Working Paper in Econometrics,
No. 40.
Binhadi and Meek, P.(1989). “Implementing Monetary Policy in Indonesia.”, mimeograph,
November 1989.
Black, S. W. (1983). “The use of Monetary Policy for Internal and External Balance in Ten
Industrial Countries.”, in J. Frenkel ed., Exchange Rates and International Macroeconomics,
Chicago: The University of Chicago Press, 1983.
Blanchard, Olivier Jean, and Quah, Danny. “The Dynamic Effects of Aggregate Demand and
Supply Disturbances.” American Economic Review, Vol. 79, No. 4 (September 1989).
- 162 -
Bordo, M. and Jonung, L. (1987). The Long-run Behavior of the Velocity of Circulation: The
International Evidence. Cambridge: Cambridge University Press.
Brown, R. L., Durbin J., and Evans J. M. (1975). “Techniques for Testing the Constancy of
Regression Relationships over Time.” Journal of the Royal Statistical Society, series B, 37,
1975.
Calmoiris, C.W. and Domowitz I. (1989). “Asset Substitution, Money Demand, and the
Inflation Process in Brazil.” Journal of Money, Credit, and Banking Vol. 21, No. 1 (1989).
Campbell, John Y. (1991). “A Variance Decomposition for Stock Returns.” Economic Journal
Vol. 101, No. 405 (March 1991).
Campbell, John Y., and Mankiw, Gregory N. (1987). “Permanent and Transitory Components
in Macroeconomic Fluctuations.” American Economic Review Vol. 77, No. 2 (May 1987).
Campbell, John Y., and Perron, Pierre (1991). “Pitfalls and Opportunities: What
Macroeconomists should Know about Unit Roots.” National Bureau of Economic Research,
Macroeonomics Conference, Cambridge, (February 1991).
Campbell, John Y., and Shiller Robert J. (1988), “Interpreting Cointegrated Models.” Journal
of Economics, Dynamics, and Control Vol. 12, No. 2/3 (June/September 1988).
, (1991), “Cointegration and Tests of Present Value Models.”, in Engel, R. F. and
Granger, C. W. J., eds., Long-Run Economic Relationship: Readings in Cointegration,
Advanced Texts in Econometrics, Oxford University Press.
Cargill, Thomas F. (1985). “A U.S. Perspective on Japanese Financial Liberalization”. BOJ
Monetary and Economic Studies Vol. 3, No. 1 (May 1985).
Chandavarkar A.G. (1977). “Monetization of Developing Countries.” International Monetary
Fund Staff Papers Vol. 24, No. 3 (November 1977).
- 163 -
Charemza, W., and Deadman D. F. (1992). “New Directions in Econometric Practice: General
to Specific Modelling, Cointegration, and Vector Autoregression.” Edward Elgar Publishers.
Chigira, Makoto (1992). “Nihon no Keijoushushi Hendou no Jikeiretsu Bunseki (in Japanese:
Time Series Analysis of Variance of the Current Account Balance).” Nihonkeizai Kenkyu
No.24 (July 1992).
Chishti, Salom U. (1992). “Macroeconomic Modelling and Pakistan’s Economy.” Journal of
Development Economics No. 38.
Cho, Yoon-Je and Cole David G. (1992) “The Role of the Financial Sector in Korea’s Structural
Adjustment”, in Corbo, Vittorio and Suh, Sang-Mok eds., Structural Adjustment in a Newly
Industrialized Country: The Korean Experience, The Johns Hopkins University Press.
Christiano, Lawrence J., and Eichenbaum, Martin (1990). “Unit Roots in Real GNP: Do We
Know, and Do We Care?” Carnegie-Rochester Conference Series on Public Policy Vol. 32,
Spring 1990.
Cole, David C., and Park Yung Chul (1983). Financial Development in Korea: 1945-1978.
Harvard University Press.
Collins, Susan M., and Park, Wom-Am (1989), “External Debt and Macroeconomic
Performance in South Korea.”, in Sachs, Jeffery D. and Collins, Susan M. eds., Developing
Counties Debt and Economic Performance, Vol. 3, Country Studies - Indonesia, Korea,
Philippines, Turkey. The University of Chicago Press.
Connolly, M., and Taylor D. (1976). “Testing the Monetary Approach to Devaluation in
Developing Countries.” Journal of Political Economy, 84.
Covey, Ted, and Bessler, David A. (1992). “Testing for Granger’s Full Causality.” Review of
Economics and Statistics Vol. 74, No. 1 (February 1992).
Cuthbertson K. (1985). The Demand and Supply for Money. Basil Blackwell.
- 164 -
Cuthbertson, K., and Taylor M. P. (1987). “The Demand for Money: A Dynamic Rational
Expectations Model.” The Economic Journal Vol. 97.
Dailami,Mansoor (1992). “Korea: Successful Adjustment.”, in Vinod, Tomas, Ajay, Chhibbler,
and Mansoor, Dailami, and de Melo Jaime, eds., Restructuring Economies in Distress: Policy
Reformed and the World Bank, Oxford University Press.
Davidson, Russell, and MacKinnon James G. (1981). “Several Tests for Model Specification in
the Presence of Alternative Hypotheses.” Econometrica Vol. 49.
Dewald, W., and Johnson, H. G. (1967). “An Objective Analysis of the Objectives of Monetary
Policy.”, in D. Carson, eds., Banking and Monetary Studies, Homewood, ILL: Richard D.
Irwin.
Dohner, Robert S., and Intal, Ponciano Jr., (1989). “The Marcos Legacy: Economic Policy and
Foreign Debt in the Philippines.”, in Sachs, Jeffery D. and Collins, Susan M. ed., op. cit.
Dornbusch, Rudiger (1986). “Multiple Exchange Rate for Commercial Transaction.”, in
Edwards, Sebastian, and Liaquat, Ahamed eds., Economic Adjustment and Exchange Rates
in Developing Countries, The University of Chicago Press.
Edwards, Sebastian (1988), Exchange Rate Misalignment in Developing Countries, The
World, Bank Occasional Paper, No. 2/ New Series. The Johns Hopkins University Press.
, (1989). Real Exchange Rates, Devaluation, and Adjustment: Exchange Rate
Policy in Developing Countries, The MIT Press.
Engle, Rovert F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the
Variance of United Kingdom Inflation.” Econmetrica Vol. 50.
, (1983). “Estimates of the Variance of U.S. Inflation Based Upon the ARCH
Model.” Journal of Money, Credit, and Banking Vol. 15.
- 165 -
Euh, Yoon-Dae, and Amsden, Alice (1990). “Korea’s Financial Reform.” Korea University,
Commercial Review No. 33.
Fair, R. C. (1986). “International Evidence on the Demand for Money.”, NBER Working Paper
No. 2106.
Fischer S. (1991). “Growth, Macroeconomics, and Development.”, NBER Macroeconomic
Annual 1991, The MIT Press.
Fry, Maxwell J, Lilien, David M and Wadhwa, Wilima (1988). “Monetary Policy in Pacific
Basin Developing Countries.”, in Cheng, Hang-Sheng ed., Financial Policy and Reform in
Pacific Basin Countries, Kluwer Academics Publishers.
Fukao, Mitsuhiro (1983), Kinnyu no Kokusaika ga Kawase Reeto Hendou Oyobi Kokusai
Syuushi ni Ataeru Eikyou nitsuite (in Japanese: Effect of Financial Internationalization on
the Balance of Payments and Foreign Exchange Rate). Nihon Ginko, Kinnyu Kenkyu Vol. 7,
No. 4, (December 1983).
Furukawa, A. (1985). Gendai Nihon no Keizaibunseki (in Japanese: Monetary Analysis of
Contemporary Japanese Economy). Tokyo: Toyo Keizai Shimposha.
Gali, Jordi (1992). “How well Does the IS-LM Model Fit Postwar U.S. Data?” Quarterly
Journal of Economics Vol. 107, No. 2 (May 1992).
Gelb, Alan. (1989). “Financial Policies, Growth, and Efficiency.” Policy, Planning and
Research Working Papers, WPS 202, Country Economics Department, The World Bank.
Goldfeld, S. M., and Quandt R. E. (1978). “Some Tests for Homoscedasticity.” Journal of
American Statistical Association, 60.
Goldfeld, S. M., and Sichel Daniel E. (1990). “The Demand for Money.”, in Friedman,
Benjamin M., and Hahn, Frank H, eds., Handbook of Monetary Economics, Vol. 1,
North-Holland.
Goldsmith R. M. (1969). Financial Structure and Development. Yale University Press.
- 166 -
Gurley, John G., and Shaw, Edward S. (1955). “Financial Aspects of Economic Development.”
American Economic Review 45.
, (1960). Money in a Theory of Finance. Bookings Institution.
Hafer, R. W. and Jansen, D. W. (1991). “The Demand for Money in the United States:
Evidence from Cointegration Tests.”, Journal of Money, Credit, and Banking. Vol. 23, No. 2.
Hahm, J. H, and Choi W. G. (1988). “An Optimal Control Model for Monetary Policies in
Korea.” Bank of Korea, Quarterly Economic Review (December 1988).
Hamada, K. and F. Hayashi (1985). “Monetary Policy in Postwar Japan.”, in Ando, A., H.
Eguchi, Farmer R., eds., Monetary Policy in Our Times, Cambridge: The MIT Press.
Hamann, A. Javier (1993). “Financial Liberalization and the Information Content of Money
in Indonesia, Korea and the Philippines.” IMF Working Paper WP/93/88, (November 1993).
Hartley, Peter R., and Walsh Carl E. (1992). “A Generalized Method of Moments Approach to
Estimating a ‘Structual Vector Autoregression’.” Journal of Macroeconomics Vol. 14, No. 2,
Spring 1992.
Hausman A. C. (1978). “Specification Tests in Econometrics.” Econometrica, 46.
Hendry, D. F. (1993), Econometrics: Alchemy or Science? Basil Blackwell.
Hendry, D. F., Pagan A. R., and Sargan J. D. (1984). “Dynamic Specification.”, in Z. Griliches
and M. D. Intrilligator eds., Handbook of Econometrics, Vol. II, Amsterdam: North Holland.
Hetzel, R. L. and Mehra, Y. P. (1989). “The Behavior of Money Demand in the 1980’s.” Journal
of Money, Credit, and Banking Vol. 21, No. 4.
- 167 -
Hong, K. “A Financial Model of the Korean Economy.” Bank of Korea, Quarterly Economic
Review (December 1991).
Ito, Kazuhisa (1991). Kankoku Kinyushijou no Hatten to Kadai, Shoukenkennkyu. Vol.100.
Ito, Takatoshi (1988). “Use of (Time-Domain) Vector Autoregressions to Test Uncovered
Interest Parity.” The Review of Economics and Statistics Vol. LXX, No. 2 (May 1988).
, (1991). Kawase Reto to Kokusai Syuusi (in Japanese: Exchange Rate and
Balance of Payments), Okurasyou Zaiseikinyuu Kenkyusyo, Financial Review (March 1991).
, (1992). The Japanese Economy, The MIT Press.
Iwabuchi, J. (1990). “Kinyuhensu ga Jittaihensu ni Ataeru Eikyo nituite (in Japanese: On the
Effects of the Monetary Variables on the Real Economic Variables).” Bank of Japan, Kinyu
Kenkyu, Vol. 9, No.3.
Iwamoto, Yasushi, and Kobayashi, Hideyuki (1994). “Testing for a Unit Root in Japanese
GNP.” Japan and the World Economy Vol. 4.
Johansen, Soren (1991). “Estimation and Hypothesis Testing of Cointegration Vectors in
Gaussian Vector Autoregressive Models.” Econometrica Vol. 59, No. 6 (November 1991).
Johansen, Soren (1991). “Statistical Analysis of Cointegration Vectors.”, in Engel, R. F., and
Granger C. W.J., eds., op. cit. 1991.
Judd, J. , and Scadding, J. (1982). “The Search for a Stable Demand Function: A Survey of the
Post-1973 Literature.” Journal of Economics.
Kama, K. (1987). “Kinyuseisaku no Mokuhyo to Kouka (in Japanese: On the Objectives and
Effects of the Monetary Policy).” Nihon Keizai Kenkyu No. 17.
- 168 -
Kawasaki, Y. (1991). “Bayesian Vetor Autoregression (in Japanese)” Bank of Japan, Kinyu
Kenkyu Vol.10, No.1.
, (1992). “Johansen no Kyouwabunkenntei nituite (in Japanese: On the
Cointegration Test of Johansen’s Method).” Bank of Japan, Kinyu Kenkyu Vol. 11, No.2.
Keating, John W. (1990). “Identifying Var Models under Rational Expectations.” Journal of
Monetary Economics Vol. 25, No. 3 (June 1990).
Keun, H. S. (1983). “Behavior of the Money Multiplier and Control of the Money Stock in
Korea.” Bank of Korea, Quarterly Economic Reveiw, (September 1983).
Khan, Mohsin (1990). The Macroeconomic Effects of Fund-Supported Adjustment Programs.
International Monetary Fund, Staff Papers Vol.37, No.2 (June 1990).
Kharas, Homi J, and Kiguel, Miguel A (1988). “Monetary Policy and Foreign Debt: The
Experiences of the Far East Countries.”, in Cheng, Hang-Sheng eds., op. cit. 1988.
Kim, J. C., Hur J. S., and Kim K. B., (1991) “Structural Changes in the Velocity of Money.”
Quarterly Economic Review, (March 1991).
Kim, M., and Hur, J., and Kim, K. (1990). “Structual Changes in the Velocity of Money.”
Quarterly Economic Review.
Kim, Sungmin and Oh, Ho II (1991). “Interest Rates and Economic Activity: A VAR
Approach.” Bank of Korea, Quarterly Economic Review, (March 1991).
King, Robert G., and Watson, Mark W. (1992). “Testing Long Run Neutrality.” National
Bureau of Economic Research, Working Paper 4156, (September 1992).
King, Robert, Plosser, Charles, Stock James, and Watson Mark (1991). “Stochastic Trends
and Economic Fluctuations.” American Economic Review Vol. 81, No. 4 (September 1991).
- 169 -
Kitasaka, S. (1993). “Nihonkeizai ni Okeru Kozohenka to Keikihendo (in Japanese: On the
Structural Change and Business Cycle of the Japanese Economy).” The Journal of the Japan
Association of Economic and Econometrics Vol. 44, No. 2 (June 1993).
Komatsu, M. (1992). “Monetary Policies and Money Markets in Indonesia.”, mimeograph,
(March 1992).
Kunitomo, Naoto and Yamamoto, Taku (1986). Tahenryou Jikeiretsu ni Okeru Ingakankei to
Kasetsu Kentei Oyobi Makuro Keiryou Bunseki heno Ouyou (in Japanese: Causal Ordering,
Testing Hypothesis in Multi-variable Time Series and its Application on Macro-economic
Analysis), Tokyo Daigaku Keizaigaku Ronsou 51-4.
Kosuge, N. (1991).
“Indonesia Keizai no Keiryo Model (in Japanese: Macroeconometric
Model of the Indonesian Economy).” Ajia Keizai Kenkyusyo, Ajia Keizai Vol. 32, No. 2.
Koumura, C. (1985). Nihon Keizai to Kinyuseisaku (in Japanese: Monetary Policy and the
Japanese Economy). Toyo Keizai Shimpousha.
Krugman, P., Alm, J., Collins, S. M., and Remolona (1992). Transforming the Philippine
Economy, NEDA and UNDP.
Kuroyanagi, M. (1989). Economic Development of Korea and Taiwan and their Economic
Relation with Japan, The Export-Import Bank of Japan, EXIM Review Vol. 9,No. 1 (March
1989).
Kuroyanagi, M., and K. Hamada. “Naiseiteki Seichouriron-Keiai Hatten, Kinyu Chukai to
Kokusai Sihon Ido (in Japanese: Endogenous Growth Theory-Economic Development,
Financial Intermediation and International Capital Movement).” Ookurasyo Zaisei Kinyu
Kenkyusho, Financial Review No. 27 (March 1993).
Laidler, D. E. W. (1993). The Demand for Money: Theories, Evidence and Problems, Harper
Collins College Publishers.
- 170 -
, (1966). “Some Evidence on the Demand for Money.” Journal of Political
Economy, 74.
Lane, C. E., Cole D. C., and Slade B. F. (1993). “Indonesia, 1974-1990.”, in Page, Sheila ed.,
Monetary Policy in Developing Countries, Routledge, 1993.
Layman, T. A. (1988). “Monetary Policy and Financial Reform in Korea.”, in Cheng,
Hang-Sheng ed., op. cit., 1988.
Lee, S. Y., and Jao Y. C. (1982). Financial Structures and Monetary Policies in Southeast Asia.
The Macmillan Press Ltd.
Lee, Kil Young (1984). “Monetary Policy and Selection of Main Intermediate Target.” Bank of
Korea, Quarterly Economic Review (September 1984).
Lucas, R. E. Jr. (1988). “On the Mechanics of Economic Development.” Journal of Monetary
Economics, 22.
, (1988). “Money Demand in the United States: A Quantitative Review.”
Carnegie-Rochester Conference Series on Public Policy, 29.
Lutkepohl, Helmut (1991). Introduction to Multiple Time Series Analysis, Springer-Verlag.
Lutkepohl, Helmut, and Reimers Hans-Eggert (1992). “Impulse Response Analysis of
Cointegrated Systems.” Journal of Economic Dynamics and Control Vol. 16, No. 1 (January
1992).
Maddala, G. S. (1983). Limited -Dependant and Qualitative Variables in Econometrics,
Cambridge University Press.
, (1992). Introduction to Econometrics; Second Edition, MacMillan Publishing
Company.
Manchester, Joyce (1989). “How Money Affects Real Output.” Journal of Money, Credit and
Banking Vol. 21, No. 1 February.
- 171 -
Mckinnon, Ronald I. (1973). Money and Capital in Economic Development. Washington, DC:
Brookings Institution.
Meltzer, Allan H. (1963). “The Demand for Money: Evidence from the Time Series.” Journal of
Political Economy, 71.
, (1986). Lessons from the Experience of Japan and the United States under
Fixed an Fluctuating Exchange Rates. Bank of Japan, BOJ Monetary and Economic Studies
Vol.4, No.2 (October 1986).
Miller, S. M. (1991). “Monetary Dynamics: An Application of Cointegration and
Error-Correction Modeling.” Journal of Money, Credit, and Banking Vol. 23, No. 2 (May
1991).
Molana, H. (1991). “The Time Series Consumption Function: Error Correction, Random Walk,
and Steady State.” The Economic Journal 101.
Nasution, Anwar. (1989). “Recent Deregulation of the Banking Sector in Indonesia.” A Paper
Prepared for a Conference on Privatization: Lessons from Europe and ASEAN, jointly
sponsored by the European Institute of Public Administration of Maastricht, Holland and
ISEAS of Singapore 16-18, February.
Nelson, Charles R., and Plosser Charles I. (1982). “Trends and Random Walks in
Macroeconomic Time Series: Some Evidence and Implications.” Journal of Monetary
Economics Vol. 10, 1982.
Odano, J. (1990). “Indonesia no Money Market (in Japanese: On Money Market of
Indoneisa).” In Rooyama S. ed., Ajia no Tanki Kinyu Shijou (Money Market in Asia),
Nihonkeizai Kenkyu Sentaa, Kenkyu Hokoku No. 76 (April 1990).
Okina, Kunio (1985). “Granger no Inngakankei wo Mochiita Jishoubunnseki no Saikentou (in
Japanese: Reexamination of the Empirical Studies Using Granger’s Causality).” Bank of
Japan, Kinyu Kenkyu Vol. 4, No.4.
- 172 -
Okuda, H. (1993). “The Changing Patterns of International Capital Flows in NIEs and
ASEAN Countries.” Paper presented at MOF-NBER Joint Conference held in Tokyo on
March 5, 1993.
, (1991). “Kinyu Jiyuka to Keizaihatten: Philippine to Tai no Hikakujirei
Kenkyu (in Japanese: On the Financial Liberalization and Economic Development:
Philippine and Thailand).” Export-Import Bank of Japan, Kaigaitousikenkyushohou Vol. 17,
No. 2 (February 1991).
, (1990). “Financial Factors in Economic Development: A Study of the Financial
Liberalization Policy in the Philippines.” The Developing Economies XXVIII-3 (September
1990).
, “Keizaihaten ni Okeru Kinyu Sector no Yakuwari (in Japanese: On the Role of
the Financial Sector in the Economic Development.”, Export-Import Bank of Japan,
Kaigaitousikenkyushohou Vol. 16, No. 5 (May 1990).
, (1989). “1980 Nendai no Firipin no Kinyushisanjyuyo (in Japanese: Monetary
Asset
Demand
of
the
Philippine
in
1980’s.”,
Export-Import
Bank
of
Japan,
Kaigaitoushikenkyushohou Vol. 15, No. 10 (October 1989).
Ookurasho Zaisei Kinyu Kenkyusho, Kinyu Shihon Shijou Kennkyukai eds., 21 Seiki he no
Bijon: Ajia no Kinyu Shihon Shijou (in Japanese: Vision for the 21st Century: Financial and
Capital Markets in Asia), 1991.
Page, Sheila, eds. (1993). Monetary Policy in Developing Countries, Routledge.
Park, J. Y. (1982). “Monetary Policy and Economic Development in Korea.” Institute of
Developing Economies Joint Research Program Series, No. 29.
Pesaran, M., Smith, P., and Yeo, J. (1985). “Testing for Structurall Stability and Predictive
Failure: A Review.” Machester School, 53.
Plosser, Charles I., and Schwert G. William (1979). “Money, Income, and Sunspots:
Measuring Economic Relationships and the Effects of Differencing.” Carnegie-Rochester
Conference on Public Policy Vol. 10 Supplement.
- 173 -
Pyndyke, Robert S., and Rubinfeld Daniel L. (1991). Econometric Models and Economic
Forecasts; Third Edition, McGraw-Hill Inc.
Quah, Danny (1987). “What Do We Learn from Unit Roots in Macroeconomic Time Series?”
National Bureau of Economic Research, Working Paper 2450 (December 1987).
Reuber, G. (1964). “The Objectives of Canadian Monetary Policy, 1949-61: Empirical Trade
Offs and the Reaction Function of the Authorities.” Journal of Political Economy 72.
Roley, V. (1985). “Money Demand Predictability.” Journal of Money, Credit, and Banking, 17.
Romer, Chistina D., and Romer David H. (1989). Does Monetary Policy Matter? A New Test in
the Spirit of Friedman and Shwartz, Blanchard, Oliver and Fischer, Stanley eds.,
Macroeconomic Annual 1989, The MIT Press.
Romer, P. M. (1986). “Increasing Returns and the Long-Run Growth.” Journal of Political
Economy 94.
Rosario, L. C. (1989). “Monetary Policy in the Philippines, 1949-1988”, The Central Bank of
the Philippines, CB Review (January 1989).
Runkle, David E. (1987). “Vector Autoregressions and Reality.” Journal of Business and
Eocnomic Statistics Vol. 5, No. 4 (October 1987).
Sachs, J. (1985). “External Debt and Macroeconomic Performance in Latin America and East
Asia.” Brooking Papers on Economic Activity 2.
Sakong, II (1993), Korea in the World Economy, Institute for International Economics.
Sakurai, Kojiro (1989). “Kinyuu kouzou to jittai keizai (in Japanese: Financial Structure and
Real Economy)”, Nihon kaihatuginko Setsubitoshi Kenkyuusho Keizaikeieikenkyuu Vol. 10-2,
Heisei Gannen 8 Gatsu.
- 174 -
Sakurai, Makoto, Hiroshi Tanaka, Masaaki Kuroyanagi, Masakazu Masuda and Astushi
Masuda (1986). “Performance of the Economic Adjustment in Developing Countries since
1970.” The Export-Import Bank of Japan, EXIM Review Vol 7, No. 1.
Shapiro, Matthew D., and Watson, Mark W (1988). “Sources of Business Cycle Fluctuations.”
National Bureau of Economic Research, Working Paper 2589 (May 1988).
Shaw, E. (1973). Financial Deeping in Economic Development, New York: Oxford University
Press.
Shiba, T. (1991). “Statistical Inference of the Japanese M1 and M2 Money Demand
Functions.” Eeconomic Studies Quarterly Vol. 42, No. 3 (September 1991).
Shigehara, Kumiharu (1991). “Japan’s Experience with Use of Monetary Policy and the
Process of Liberalization.” BOJ Monetary and Economic Studies Vol. 9, No. 1 (March 1991).
, (1990). “Some Reflections on Monetary Policy Issues in Japan”. BOJ Monetary
and Economic Studies Vol. 8, No. 2 (September 1990).
Shin, Hyunchul (1986). “Monetary Aggregates and Economic Activity in Korea”, Bank of
Korea, Quarterly Economic Review (March 1986).
, (1978). “Money, Income and Causality: The Case of Korea.” Bank of Korea,
Quarterly Economic Review (September 1978).
Sims, Chirstopher A. (1992). “Interpreting the Macroeconomic Time Series Facts: The Effects
of Monetary Policy.” European Economic Review Vol. 36, No. 5 (June 1992).
, (1972). “Money Income, and Causality.” American Economic Review 62
(September 1972).
- 175 -
Sims, Christopher A., Stock, James H., and Watson, Mark W. (1990) “Inference in Linear
Time Series Models with Some Unit Roots.” Econometrica Vol. 58 No. 1 (January 1990).
Skully, M. T. ed. (1992). “Financial Institutions and Markets in the Far East: A Study of
China, Hong Kong, Japan, South Korea and Taiwan.” St. Martins Press.
Spencer, David E. (1989). “Does Money Matter? The Robustness of Evidence from Vector
Autoregressions.” Journal of Money, Credit, and Banking Vol. 21, No. 4 (November 1989).
Stock, James H. (1990). “Unit Roots in Real GNP: Do We Know and Do We Care?: A
Comment.” Carnegie-Rochester Conference Series on Public Policy Vol. 32, (Spring 1990).
Stock, James H., and Watson Mark W. (1990). “Interpreting the Evidence on Money-Incocome
Causality.” Journal of Econometrics, Vol. 40, No. 1, (January 1990).
Stulz, Rene M., and Wasserfallen Walter. “Macroeconomic Time-Series, Business Cycles and
Macroeconomic Policies.” National Bureau of Economic Research, Working Paper 4217
(November 1992).
Sudo, M. (1990). 1980 Nendai no Kankoku no Manee Maaketto no Tenkai (in Japanese:
Development of the Korean Money Market in 1980’s) in Rooyama S., eds. Ajia no Tannki
Kinnyuu Shijou (Money Market In Asia), Nihon Keizai Kennkyuu Sentaa, Kenkyuu
Houkoku,No.76 (April 1990).
, (1990). “Chuouginko to Mane Maaketto no Douko (in Japanese: On Central
Bank Policy and Money Market in Philippine).” In Rooyama, S. ed. (1990), op. cit.
Suleik, M. B., “Foreign Exchange Liberalization: A Milestone in Our Move Towards and
Out-ward-looking Economy.” The Central Bank of the Philippines, CB Review (November
1992).
- 176 -
, (1991). “Banking and Financial Reforms in the Philippines (Part 1).” The
Central Bank of the Philippines, CB Review (September 1991).
Sundarajan, V. and Molho Lazaros (1988). “Financial Reform and Monetary Control in
Indonesia.” In Cheng, Hang-Sheng ed., op. cit..
Suzuki, Yoshio (1987). “Financial Reform in Japan-Developments and Prospects.” BOJ
Monetary and Economic Studies Vol. 5, No. 1 (December 1987).
Suzuki, Yoshiharu (1986). “A Comparative Study of Financial Innovation, Deregulation and
Reform in Japan and the United States.” BOJ Monetary and Economic Studies Vol. 4, No. 2
(October 1986).
Taguchi, H. (1993) “Financial Deeping and Economic Growth in the Asia-Pacific Region: A
Lesson from Financial Deregulation in Indonesia.” Working Paper No. 31, Department of
Research Cooperation, Economic Research Institute, Economic Planning Agency, Japan,
(August 1993).
Takeuchi, Yoshiyuki (1989). “Trends and Structural in Macroeconomic Changes Time Series.”,
mimeograph.
Tesfatsion, L. and Veitch J.M. (1990). “U.S. Money Demand Instability: A Flexible Least
Squares Approach.” Journal of Economic Dynamics and Control 14.
Tobin, J. (1955). “Multiple Probit Regression of Dichotomous Economic Variables, Cowles
Foundation Discussion Paper, No. 1, 1955 (Unpublished).” In Collective Paper of James
Tobin.
, (1958). Estimation of Relationships for Limited Dependent Variables,
Econometrica 26 (January 1958).
Todd, Richard M. (1990). “Vector Autoregression Evidence on Monetarism: Another Look at
the Robustness Debate.” Federal Reserve Bank of Minneapolis, Quarterly Review Vol. 14, No.
2, (Spring 1990).
- 177 -
Toyota, T. (1990). “Nihon no Kahei Juyou, 1900-1988 nen.”, (in Japanese: On the Money
Demand for the Japanese Economy, 1900-1988), Kokumin Keizai Zashi, 162.
Tseng, Wanda, and Robert Corker (1991), “Financial Liberalization, Money Demand, and
Monetary Policy in Asian Countries.” International Monetary Fund, IMF Occasional Paper
84 (July 1991).
Tsutsui, Y., and M. Hatanaka. (1982). “Nichibei Ryoukoku ni Okeru Kahei Jyuyoukansuu no
Anteisei ni Tsuite.”, (in Japanese: On the Stabilities of the Money Demand Function in the
Japan and the U.S.), Kikan Riron Keizaigaku 50.
Ueda, Kazuo (1983). Kokusai Makurokeizaigaku to Nihonkeizai (in Japanese: International
Macroeconomics and Japanese Economy) Chapter 4, Toyokeizai Shinpousha.
, (1992). “Kinyuu Seisaku no Seiji Keizaigaku (Political Economy of Monetary
Policy).” in ‘Kokusaishuusi Fukinnkooka no Kinyuu Seisaku (Monetary Policy under Balance
of Payment Disequilibrium)’, Toyo Keizai Shimposha.
van Winbergen, Sweder (1986). Exchange Rate Management and Stabilization Policies in
Developing Countries, in Sebastian Edwards and Liaquat eds. Economic Adjustment and
Exchange Rates in Developing Countries, University of Chicago Press.
Yap, J. T. (1991). “A Perspective on Philippine Economic Performance and Development
Strategies.” Working Paper No. 3, Department of Research Cooperation, Economic Research
Institute, Economic Planning Agency, Japan, (July 1991).
Ygrubay, Anthony. P. “Government Securities and Monetary Policy.” The Central Bank of the
Philippines, CB Review (July 1990).
Yoshikawa,
Hiroshi.
(1993).
“Makuro
Keizaigaku
to
Nihonkeizai.”,
(in
Japanese:
Macroeconomics and the Japanese Economy), Tokyo: Toyo Keizai Shinposha.
, (1992). Nihon Keizai to Makuro Keizaigaku (in Japanese: Japanese Economy
and Macro Economics), Toyo Keizai Shimpousha.
- 178 -
, (1989). “Money Supply to Jittai Keizai (in Japanese: Money Supply and Real
Economy).” Tokyo Daigaku Keizaigaku Ronsou 55-3 (October 1989).
White, Halbert (1980). “A Heteroskedasticity-Consistent Covariance Matrix Estimater and a
Direct Test for Heteroskedasticity.” Econometrica Vol. 48.
Woo, W. T., and Nasution A (1989). “Indonesian Economic Policies and Their Relation to
External Debt Management.” Vol. 3 in Developing Country Debt and Economic Performance
edited by Sachs, J. D. and Collins S. M. op. cit..
World Bank (1993). The East Asian Miracles: Economic Growth and Public Policy. Oxford
University Press.
, (1991). World Development Report 1991. Oxford University Press.
Zialcita, E. P., and Gonzalez, C. M. (1991), “The Conduct of Monetary Policy in the
Philippines: 1971-1990”, The Central Bank of the Philippines, CB Review (May 1991).
- 179 -