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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. - 82 - 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 - 83 - (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. - 84 - 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). - 85 - 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. - 86 - 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. - 87 - 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. - 88 - 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 - 89 - 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 - 90 - 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. - 91 - FIG. 3.1: CHOW TEST JAPAN, 1963: I - 1972: I FIG. 3.2: CHOW TEST JAPAN, 1974: II - 1989: I - 92 - FIG. 3.3: CHOW TEST KOREA, 1974: III - 1978: IV FIG. 3.4: CHOW TEST KOREA, 1981: I - 1989: I - 93 - FIG. 3.5: CHOW TEST INDONESIA, 1978: I - 1982: IV FIG. 3.6: CHOW TEST INDONESIA, 1984: II - 1989: I - 94 - 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). - 95 - 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 - 96 - 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 - 97 - (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. - 98 - 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 - 99 - 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 - 100 - 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 - 101 - 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 - 102 - 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. 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