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OVERNIGHT MONEY-MARKET INTEREST RATES,
THE TERM STRUCTURE
AND THE TRANSMISSION MECHANISM
Colin Hargreaves
No. 62- November, 1991
ISSN
0 157-0188
ISBN
0 85834 981 7
A number of studies (e.g. Battelino and McMillan, 1989,
Bewley and Elliott, 1988, Macfarlane, 1988, and Stevens and
Thorp, 1981) have analysed the effects of deregulation upon
different financial aggregates, the transmission mechanism and
the effectiveness of interest-rate based monetary policy;
these studies mostly use data up to early 1988. Since then
there has been the change from SRD’s to Non-callable deposits,
the use of high interest rates by the government and also,
last but not least, there has been the stock market crash.
Many countries have also experienced the effects of increasing
financial integration, the rise of offshore markets and so on.
The aim of this paper is to see whether the same relationships
persist or whether there has been some structural change
within the financial sector.
There are four separate analyses, the relationship
between unofficial and official overnight cash rates, the
relationship of Australian to foreign rates, the term
structure of rates and the transmission mechanism.
Overniqht Rates.
Bewley and Elliott (1988) analyse the relationship
between the official and unofficial overnight funds rate.
Since Elliott(1987) had found a strong relationship between
other interest rates and the unofficial overnight rate, they
argued that a test of the relationship between the unofficial
and official rates is implicitly a test of the Bank’s ability
to control interest rates.
Using the idea of cointegration, Bewley and Elliott
analysed a supposedly "clean" period of post-float data from
February, 1984, to June, 1986. By "clean" they meant a period
in which the Bank ’did not intervene in foreign exchange
markets but targetted interest rates in their conduct of
monetary policy’. Whether any period is truly "clean" is
possibly debateable. However they found a clear stable longrun relationship between the two interest rates where
essentially the unofficial equalled the official plus a
differential of 1%. In the short-term, deviations from this
differential of 1% affected daily changes in both rates,
drawing them back towards the long-run relationship. They
found ’a short-run dynamic policy reaction function which
indicates that the official rate reacts to the size of the
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past differential’. There is a possible problem of direction
here for it is not clear whether the official rate is
responding to the unofficial rate or vice-versa.
As a first analysis of changes over the last few
years, recent daily data on interest rates were kindly
provided by Bob Rankin at the RBA and this was used to see if
the relationships found in Bewley and Elliott remain today.
While the Bewley and Elliott data set ended in June, 1986, the
following analysis was carried out both with all the data
available, i.e. from July, 1986, to March, 1990, and also with
consecutive six month sections, April to September and October
to March. This timing fitted well with a number of policy and
regulatory changes that occurred around the months of April
and September.
The searchfor a stable long-run relationship
between non-stationary variables involves seeing whether the
variables are cointegrated. Two variables are cointegrated
if, even though they are both individually non-stationary
(i.e~ trending strongly, not naturally returning to a mean
value), a linear function of them exists which is stationary
(i.e. not trending and tending naturally to return to an
overall mean). Normally a linear function of non-stationary
variables is also non-stationary. Thus if instead of this, one
finds a linear function of the variables that is stationary,
this implies that this function probably catches some form of
long-run equilibrium relationship between the variables. The
fact that the function is stationary implies that errors from
the relationship tend to return automatically towards zero
(i.e. do not trend away) and from this arises the idea of a
stable long-run relationship. This is not to say that a
stable long-run relationship cannot exist between two
stationary variables but rather that if the variables are
nonstationary, then this requirement is a good test of whether
a long-run relationship exists.
Rather than report all the technical results in this
paper, let me say that they are available in a supplement to
the paper on request. Firstly if we look at the graphs of the
two variables, the unofficial and official overnight cash
rates (Figure i), we see two variables that are typical I(1),
non-stationary variables. An I(k) variable is a variable that
needs to be differenced k times for the result to be
stationary. Dickey-Fuller and Durbin-Watson based tests show
clearly that the two variables are nonstationary but their
first differences are stationary and so the two variables are
I(1) variables.
As Bewley and Fiebig(1989) state, there can be quite
a disparity between various estimates of the cointegrating
relationship. Table i shows results from four different ways
of estimating the relationship, including DW statistics.
Firstly it appears that the two variables are cointegrated; a
rough guide is that if the DW statistic in a two variable
relationship is above 0.4 then the variables are cointegrated
on a 5% test.
Table 1
Unofficial on official
Sample
All
70 200330460590720850-
B1
B2
Official on unofficial
DW
B1
B2
DW
199
329
459
589
719
849
979
0.96
1.21
1.05
0.87
0.73
1.00
0.77
1.05
1.02
-3.06
-0.30
1.95
3.91
0.17
4.51
-0.72
0.66
1.47
0.71
0.77
0.99
1.22
0.56
0.37
1.00
0.60
0.85
0.69
0.69
1.00
0.41
0.91
-0.48
6.32
1.54
2.76
3.09
-0.Ii
9.83
1.47
0.63
0.90
0.18
0.80
0.45
1.21
0.89
0.37
0.96
0.72
0.90
0.61
0.51
0.99
0.31
0.95
200- 459
460- 719
720- 979
0.94
0.82
0.86
1.19
2.79
2.88
0.69
0.86
0.41
0.97
I.II
0.72
-0.14
-1.93
4.47
0.65
0.76
0.42
0.91
0.91
0.62
Weekly data
All
0.96
April 89
- Mar 90 0.89
0.99
0.57
1.01
0.56
0.98
2.32
0.25
0.75
0.29
0.67
4
Table 1 cont
Bewley Transformation
Unofficial on official
Sample
All
70 i00300460590720850-
199
329
459
589
719
849
979
Official on unofficial
B1
B0
DW
B1
0.96
1.15
1.06
0.87
0.71
1.00
0.26
1.05
1.01
-2.23
-0.36
1.93
4.15
0.19
13.57
-0.68
0.27
0.74
0.41
0.32
0.44
0.67
0~86
0.17
1.01
0.68
0.86
0 .69
0 .78
1 .00
0 .65
0 .91
B0
-0.58
4.90
1.35
2.78
2.06
-0.15
5.36
1.39
DW
R2
0.27
0.51
0.39
0.38
0.25
0.66
0.45
0.16
0.97
0.84
0.92
0.69
0.63
1.00
0.39
0.96
However although on average the relationship appears
to be one where the unofficial equals the unofficial plus 1%,
from the sub-sample results there is clearly great variation.
If one looks at the last year in particular, the two interest
rates may no longer be cointegrated and the coefficient on the
explanatory variable has dropped significantly below one. If
one looks at the graph again of the interest rates, we can see
that in the middle of last year the two variables remained
surprisingly far apart for some while. This is not just a
seasonal effect for, although at the same time in the previous
year the two rates stayed apart for some while, this did not
happen two years before. Rather the market felt that the
economic indicators looked bad and, although the RBA may have
tried to withold the rise by keeping official rates steady,
eventually the Bank followed on.
To look at the relationship between the two cash
rates more carefully, the Beta (slope) coefficient for the
regression of Unofficial on Official rates was calculated for
successive samples of size 130 (half years) like a moving
window moving through the overall sample of 979 observations
(3.5 years). Figure 2 shows a graph of the successive Beta
coefficients and although there is great variation over the
last two years, one can see a general trend downwards. Figure
3 plots the constant term from the relationship and again one
5
can see a general change. Finally Figures 4 and 5 show how the
Durbin Watson statistics and R2’s have changed.
These graphs raise the possibility that the old
relationship is breaking down and hence whether another
variable should be involved in the long-run relationship. One
candidate might be Banks’s Lending To Dealers, BLTD, in that
this may reflect changes in the money market dealers’
position. A graph of this variable is shown in Figure 6 where
we note how BLTD suddenly halved about the middle of last
year. When this variable is added to these moving window
regressions, the t-values resulting show a great change in the
last year (see Figure 7). These t-values should not be
interpreted in relation to the t-distribution since we have
non-stationary variables but it is clear however that last
year this variable became significantly more important than it
had been before. In the period approaching the tax run-down,
their is a massive rise in cash reserves which then called
upon when required. Hence BLTD drops for tax reasons but also
as the differential opens up, it is better to lend on the
market rather than to dealers. Changes in PAR may have caused
the differences shown across the years.
One problem with this relationship between the two
cash rates is that although the unofficial rate can drop
temporarily below the official rate, generally speaking there
is an inequality relationship between the two rates where the
unofficial should be higher than the official. Figure 8 shows
a crossplot of the two rates. The extreme value at the top is
when unofficial rates reached 23%, some 5% above official
rates! The extreme value at the bottom is the 346th
observation (the ’Crash’) when unofficial rates dropped to
8.5% and official rates were 7.58%. There are two other
’outliers’ in the sense that the official rates were below the
official rates and these were on observation 931 when
unofficial rates were 17.5% and official rates were 17.79 and
on observation 948 when unofficial rates were 16.5% and
official rates were 16.71. Both these relate to times when the
government reduced rates.
This plot raise great problems in my mind about
estimating a cointegrating relationship in a normal manner.
There is essentially a frontier here where unofficial equals
official and except in exceptional circumstances this is not
6
transgressed. However this means that the error structure
around the cointegrating relationship is extraordinarily nonnormal because of a restraint of the errors on one side of the
relationship. Here we are into an area where the econometric
theory is unknown at present. However the above analysis does
appear to show that the old relationship between the two rates
may be changing such that, in a sense, the RBA has less
control over unofficial rates.
Finally given the obvious relationship that the
unofficial equals the official plus a differential, the
unofficial was regressed on the official while constraining
the slope term to unity. Table 2 shows the results using
consecutive years of data. Three things emerge. Firstly the
R-squared decreases from year to year and secondly the DurbinWatson also decreases. This shows how not only is the
relationship becoming weaker but also less likely to be
cointegrated. Finally the constant here just represents the
mean differential and here we see how on average the
differential has decreased from year to year. Figure 9 shows
a graph of the actual differential over the last few years and
while it did rise sharply in the middle of last year, one can
see how if one took years as a whole the two rates have tended
to stay closer together over the last year.
Table 2
Sample
200 April
460 April
720 April
constant
459
87 - March 88
719
88 - March 89
979
89 - March 90
R2
DW
0.49935
0.91
0.68
0.45846
0.77
0.55
0.41962
0.60
0.44
In sum, what is the overall conclusion of this
analysis. Two contrasting patterns emerge. On the one side
the relationship between the two variables appears to be
weakening but on the other side they appear to be closer
together than before. Is this because the market is growing
and so increased competition is forcing the rates closer
together? Equally an increased volume and number of players in
the market might have made it less easy to control. Whatever
the reason, it would appear that there have been some changes
in the relationship but the bottom line may still be much the
same.
The Term Structure
From looking at the relationship between the two
overnight rates, we now turn to the relationship between these
and longer term rates. Under the term structure approach,
long-term interest rates are supposed to represent peoples’
expectations about shorter rates over the appropriate relevant
period. One test of this hypothesis is to see whether the 90day holding yield on a 10-year stock equals the yield on a 90day stock. If the exact expectational hypothesis is correct
then no other variables should be significant if they are
added to this relationship.
Malcolm Edey (Macfarlane, 1988) analysed the term
structure by relating the daily holding yield of i0 year stock
to a short term yield, the yield differential, the US yield
and the change in the exchange rates. When I used a small
half year sample I obtained similar results in that the short
term yield and the yield differential were the only
significant variables. However on using the whole sample
period, the result completely vanished with R-squared dropping
from 45% to 2%. The problem is that there are some very
extreme outliers in the data which dominate the results;
Malcolm Edey commented to me that this was one reason why he
did not go further with the analysis. His and my analyses
used the running or implicit yields published by the RBA. It
may be that this is not very fruitful and that instead it may
be better to use specific individual stock prices. If I could
obtain this data I would be happy to test the expectational
term structure hypothesis.
As this approach did not work well at all, to gain a
picture of what is happening, Table 3 shows some simple
correlations between rates. Possibly the most striking thing
in the table is the way that the correlation between the 90day bond yield and the ten year stock has dropped dramatically
over the last year. Also if one looks at Figure i0, one can
see how the 10-year yield wanders across the graph almost
unaware of the great changes in the short term rates. As
banks have become more risk averse, they may feel that it is
very hard toform any useful or reasonable expectations about
the next ten years and so the long-term market may carry on
almost irrespective of the short term market. Another
possibility is that there could be a fair amount of market
segmentation in that the market participants may be different
for long as compared to short term stocks.
90 day bills
Unofficial Cash
Sample
Official
90-day
10-year 10-year US 3month
All
0.98
0.97
0.65
0.68
0.47
86:10-87:3
0.85
0.71
0.27
0.52
-0.18
87:04-87:9
0.95
0.92
0.48
0.63
-0.79
87:10-88:3
0.78
0.70
0.52
0.83
-0.18
88:04-88:9
0.71
0.69
0.37
0.35
0.88
88:10-90:3
1.00
0.95
0.87
0.94
0.96
89:04-89:9
0.56
0.63
-0.40
0.11
-0.53
89:10-90:3
0.98
0.92
-0.05
0.22
-0.59
Table 3: Correlations between the Unofficial Cash Rate and
3 other rates and between the 90 day bill rate and
two other rates for different samples.
The key issue for the RBA is which rate effects
activity most, the long or the short, for it appears that they
have little or no control over the long rates. This may in
turn depend on the uncertainty in the market. With present
high uncertainty, most dealings are at the short end as no one
wants to take a long risk. As far as this is concerned, the
present monetary policy has bitten hard. However if major
business investment worked through long term loans then
government policy would have taken longer to bite. Another
major factor here, is the ability of larger corporations to
borrow abroad, but as interest rates have also risen abroad
this may not prove so useful.
Financial Inteqration and Uncovered Interest Parity.
While term structure relationships are about
interest rates within one country, it is argued that another
determinant of domestic interest rates is foreign rates,
especially with the greater freedom of capital movements from
one country to another. Such capital movements also effect
the exchange rate in the process. The dominant theory on the
exchange rate and interest rates is presently uncovered
interest parity. For example, Wallis(1990) studies English
interest rates and finds that a real interest parity
relationship with a risk premium encompasses other
hypothesised relationships. The major trouble in Australia in
estimating such a relationship is that we do not have a
monthly consumer price index. For Dirk Morris’s work in
Macfarlane(1988) a monthly price index was created probably by
interpolation.
Some studies have found that a nominal interest
parity relationship works fairly well. Given daily data, this
was the only relationship one could try. Given US 3-month
(P~s0~) and Australian 90-day (RAu0~) rates and the exchange rate
(e~) the nominal interest parity relationship is of the form
RAU,t =
Rg$,t * et / et+9o
The argument is simply that if you took your money to the USA,
invested it for 3 months and then brought your money back into
Australian dollars, in the long run, you should not be able to
make a profit; if you could, market pressures would force
changes in the rates until the profitable difference had been
removed.
This is really a long-run equilibrium hypothesis
which applies across policy changes, temporary changes in
expectations and so on. Thus it will not come as a great
i0
surprise to you that over the last three years this
relationship did not appear to work at all. Without taking
first differences, the Durbin-Watson statistic was as low as
0.0089 and so the variables were not cointegrated and when
first differences were taken, then there was a very weak
negative relationship, the reverse of that expected.
Immediately one added the first difference in the unofficial
cast rate, the R-squared jumped to 0.3 and the coefficient on
the unofficial rate was very significant. Thus on a daily
basis the uncovered interest parity equation does not work.
However, when one uses quarterly data there is a fairly
reasonable fit and I shall be reporting this in my paper to
the Economic Modelling of Australia Conference, on June 14/15
in Canberra. As a final comment, Dirk Morris’s conclusions
might have changed if he had used the expected change in the
exchange rate in his~equations.
From Figure i0, which showed the official cash rate,
ten year bond rate and the US 3 month bond rate, it seems
evident that within this period there is no clear relationship
between them. Now let us look at two graphs that may be
indicative of major structural changes that have occurred over
the last few years. Figure ii shows a graph of the Australian
90 day rate against the US 3 month rate adjusted for the
changes in the exchange rate over 3 months. The data
initially pan out an almost vertical relationship on the left
and then with the stock market crash, the data start moving
off in a completely new direction; then they appear to start
to return but halt and may be settling about a new position.
If one graphs the exchange rate against the difference of the
two rates (see Figure 12), again one finds a sudden change in
direction after the stock market crash. As more post crash
data becomes available, it may be able to identify the reasons
behind these changes.
In summary, there does appear to be relationship
between Australian and foreign interest rates but this is a
distinctly long-run relationship and not to be found by
analysing daily rates just over the last few years. With the
growth in financial integration and high interest rates
abroad, Australia may have to fall in line with foreign
markets more and more and then the RBA/government will have
less power to manipulate the economy through interest rates.
ii
Transmission
Finally I move further up to look at the
relationships between interest rates, monetary aggregates and
real effects in the economy. There have been a number of
studies of the question ’Does money matter?’. The RBA
position appears to be that it doesn’t, or at least that the
relationships are so unreliable that they are unusable.
Instead we have the approach where a ’broad perspective’ is
taken in assessing the ’needs’ of the economy. Peter
Stemp(1989) has even considered a weighting system for the
various indicators.
It is argued that a major difficulty of this
approach is the lack of certainty in the market in that there
are no set rules. This is debatable for if one does try to
have a rule that fixes one variable on a certain path it is
normally at the cost of volatile movements in another variable
that has to compensate in order to keep the aggregate on
track. Vice versa an experienced dealer no doubt gets a
feeling as to which way he expects the RBA to move. In both
the past two mid-year periods, the market seemed to reach a
consensus that rates would have to rise and so they did, with
the official rates following on eventually by force majeur.
When interest rates are on the rise, it may well be the RBA
has little real control. Vice versa when they are falling the
RBA can hold rates up until each time they announce another
drop because of the inequality relationship between the two
cash rates.
The standard way of seeing whether ’Money matters’
is to estimate a Vector Autoregressive Model (VAR) between a
few key variables such as a money aggregate, a key interest
rate, GDP, employment and inflation. With deregulation in
many countries, the higher monetary aggregates, such as
credit, broad money and even M3 have shown some fairly wild
fluctuations. Ric Battellino and Nola Macmillan’s (1989)
paper documents this well.
Bullock, Morris and Stevens (1989) and Stevens and
Thorp (1989) investigate the relationship between financial
12
indicators and economic activity. A minor point is that they
use ’graphical comparison and simple correlation coefficients
... to see which variables have a reasonably reliable
relationship with private demand’. It should be said that
graphs should never be used to prove the existence of a
relationship but rather only to detect the possibility of one
and to explain an otherwise proven relationship. It is also
very worrying when someone says about a graph that an isolated
period ’the fourth quarter of 1975 .. is probably best
regarded as an aberration’. It so happens that this
aberration relates much better to a definite downturn in M1
than a rather marginal rise in interest rates.
Furthermore one must be very careful when analysing
correlations between variables at different lags if one has
not also analysed the non-stationarity of the variables and,
above all, t-values-for correlations between non-stationary
variables are very misleading. The abstract concludes that
’The evidence for monetary and credit aggregates is mixed. M1
tends to lead private spending (though this is not independent
of interest rates) .... One consistent theme which comes
through in the results is that interest rates are a reasonably
good leading indicator of changes in demand, particularly real
demand.’
Before Glenn Stevens jumps up in defence let me
immediately add that he takes care of these problems by
carrying out a detailed statistical analysis in his paper,
Stevens and Thorp(1989). One further comment however is that
if you are going to carry out tests on long lag relationships,
Ken Wallis showed some while ago now that you should
definitely use seasonally unadjusted data. The results on
seasonally unadjusted data in Appendix B are rather more mixed
than those on the adjusted data with about 40% fewer
significant coefficients and only one in six coefficients
being significant. Nowadays instead of Granger causality
tests one would expect an analysis of cointegration, but
regrettably in a footnote, it says that ’it is not the purpose
of this paper to pursue that issue’.
This final part attempts briefly to carry out this
analysis. Quarterly data upon GDP, the 90-day Bill rate, the
exchange rate, inflation, consumption, M1 and employment were
collected. One open exploratory way of finding whether the
13
variables are cointegrated is too look at principal components
since a zero eigen value is a sign of a linear relationship
between thevariables, the relationship being defined by the
eigen vector. The lowest eigen value found of 0.0098 relates
to a relationship just between M1 and Employment and the 90day bill rate does not appear in any cointegrating
relationship defined in this way.
There are many different possible formulations of a
VAR model with an error-correction but rather than test all
here, I shall just consider the three variables Currency, the
90-day bill rate, and prices with SGDP and GDP. Currency was
used to avoid the question of whether non-callable deposits
should or should not be excluded from MI. Firstly a logrelationship between GDP (LProd), Currency, the 90-day bill
rate (B90) and a derived price index (LPconm) was estimated
for a sample from July, 1974, to September, 1989, and for the
more recent sample form just January, 1985. Whilein both
cases the variables appear to be cointegrated (as DW>0.4), the
interest rate has a perversely positive coefficient (see Table
4). This clearly cannot reflect a true long-run relationship.
Further tests confirmed this.
Table 5 shows the estimates of a long-run
relationship without B90. The result is similar coefficients
but of opposite sign on Currency and prices and hence this
long-run relationship essentially means that there has been a
fairly stable velocity with respect to Currency. When a
vector autoregressive model was estimated with this data,
clearly fairly long lags would be required with monthly data.
Given the limitations of some statistical packages, quarterly
data was used instead.
Since real GDP was fairly close to being stationary
but SGDP was not, SGDP was used and so the cointegrating
relationship was simply
Velocity = LSGDP - LCurrency
The results shown in Table 6 are unclear. The major problem
is that the coefficients on the interest rates tend to be
positive. Further analysis could clearly be done on these
relationships. When insignificant coefficients were removed
it soon became clear that all SGDP was related to was itself
14
and lagged velocity (see Table 7). Since lagged velocity is
implicitly a relationship to lagged Currency, this would imply
that money does matter, but only relative to the long run
equilibrium.
These results are clearly based on a very simplistic
reduced form type model. To undertake a reasonable analysis
of the relationship between the money stock, interest rates,
GDP and prices, you really need a good macroeconometric model
of the economy. A reasonable macroeconometric model is
essential for a systematic analysis of the economy. These are
the real research material of EMBA which has been created to
carry out comparative analysis on these models to assess their
relative advantages and weaknesses. There is not time to go
on to discuss the models here, except that given the changes
due to deregulation there are not surprisingly some changes
required to some of the models. For a further discussion of
this, may I invite you to my paper at the conference, Economic
Modelling of Australia in Canberra on June 14/15.
Conclusion
Generally the overall picture is of a financial
sector with many changes still working there way through. We
have recently seen a sudden reduction in balance sheet growth
and a necessary relaxation in PAR. The effects of the
government surplus have still not fully unravelled. There
have been some interesting changes in the daily cash markets;
the relationship of the exchange rate to the domestic/foreign
interest rate differential is changing particularly since the
crash (the effects of the crash and fears about creditworthiness and asset values may be much greater than has been
thought to be the case); the uncertainty in the market, the
risk averse management of balance sheets and the shortage of
long-term.treasury stock has possibly created a distinct
segmentation in the market such that the expectational
hypothesis of the term structure is unlikely to be effective.
However given this, there appear to be some strikingly stable
economic relationships such as the velocity with respect to
currency even though credit aggregates have been very
volatile.
15
References
Ric Battellino and Nola McMillan (1989)
’Changes in the Behaviour of Banks and Their Implications for
Financial Aggregates’, pp124-146 in Macfarlane and
Stevens(1989).
Ronald Bewley and Graham Elliott (1988)
’The Transmission of Monetary Policy: the Relationship between
Overnight Cash Rates’, University of New South Wales, School
of Economics Discussion Paper, September.
Ronald Bewley and Denzil Fiebig (1987)
’On Estimating Long-Run Parameters of Dynamic Econometric
Models’, paper presented to the Australasian Meeting of the
Econometric Society, Christchurch, New Zealand.
Michele Bullock, Dirk Morris and Glenn Stevens (1988)
’The Relationship Between Financial Indicators and Economic
Activity: 1968-1987’, Research Discussion Paper 8805, Reserve
Bank of Australia; and pp53-85 in Macfarlane and Stevens
(198~).
Graham Elliott (1987)
’Official Cash Rates, Other Interest Rates and the Operation
of Monetary Policy in Australia’ unpublished undergraduate
thesis, School of Economics, University of New South Wales.
I.J. Macfarlane (1988)
’International Interest Rate Linkages and Monetary Policy: the
Case of Australia’, Research Discussion Paper 8812, Reserve
Bank of Australia.
Ian Macfarlane and Glenn Stevens (1989) (editors)
’Studies in Money and Credit’, October 1989 conference
proceedings, published by the Reserve Bank of Australia.
Glenn Stevens and Susan Thorp (1989)
’The Relationship Between Financial Indicators and Economic
Activity: Some Further Evidence’ p86-123 in Macfarlane and
Stevens(1989).
16
EQ( I) Modelling LProd
by OLS
The Sample is 1974(7) to 1989(9) less 0 Forecasts
VARIABLE
LCurreny
Bg0
LPconm
CONSTANT
R>~ = .6945700
RSS =
COEFFICIENT
.5034148
.0059061
-.5720834
2.0905156
STD ERROR
.06016
.00114
.08059
.20993
H.C.S.E.
.06734
.00112
.09116
.23319
t-VALUE PARTIAL
8.36859
.2812
5.20121
.1313
-7.09887
.2197
9.95796
.3565
~ =
.0359124 F(3,179) =
135.69 [ .0000]
DW = .749
.2308557460
for 4 Variables and 183 Observations
EQ(5) Modelling LProd
by OLS
The Sample is 1985(I) to 1989(9) less 0 Forecasts
VARIABLE
Bg0
LPconm
CONSTANT
LCurreny
COEFFICIENT
.0041855
-.3808347
1.7749335
.4593542
STD ERROR
.00161
.22461
.35941
.14193
H.C.S.E.
.00185
.23197
.41629
.14864
t-VALUE PARTIAL r>~
2.60589
.1136
-1.69553
.0515
4.93840
.3151
3.23650
.1650
R>~ = .6237736 ~ =
.0274303 F( 3, 53) =
29.29 [ .0000] DW =2.164
RSS =
¯ 0398784127
for 4 Variables and 57 Observations
TABLE 4.
17
EQ(9) Modelling LProd
by OLS
The Sample is 1985(I) to 1989(9) less 0 Forecasts
VARIABLE
LProd 1
LCurreny
LCurrenl
LPconm
LPconm 1
CONSTANT
COEFFICIENT
.0386429
.4049707
.0551752
-.7698913
.3586187
2.2180852
STD ERROR
.15179
.28211
.26060
.49691
.46901
.47592
H.C.S.E.
.14867
.32328
.35503
.48503
.48103
.53696
t-VALUE
PARTIAL r~
.25459
.0013
1.43553
.0388
.21173
.0009
-1.54935
.0450
.76463
.0113
4.66062
.2987
R~, = .5809407 ~ =
.0295119 F( 5, 51) =
14.14 [ .0000] DW = 1.973
RSS =
.0444185261
for 6 Variables and 57 Observations
Information Criteria: SC = -6.731566; HQ = -6.863045; FPE =
.00096
R~ Relative to DIFFERENCE+SEASONALS =
.34923
SEASONAL MEANS
.02713
-.01088
.00614
.00861
of DIFFERENCES
-.01114
.00954
are
.02052
.01215
Solved
STATIC LONG RUN Equation
LProd
=
(
.479 LCurreny
.1707O)
(
WALD Test Chit,(3) =
TABLE 5
.00716
-.02236
-.428 LPconm +
.26768)
(
698015.339
.00364
-.02368
2.307
.36843)
18
RECURSIVE UNRESTRICTED SYSTEM ESTIMATES
1
|LCurr
2 |LCurr 3 |L$GDP 1 |L$GDP 2 |L$GDP
.0592
¯2860 I |LCurr
.0107
.3156
-.0394
-.0174
-.04
-. 4378
¯ 4723
-.3640
-.1628
-.1335
-.1190
-.21
.002146
-.0573
.7007
.2051
.0648
.004073
.01
1
|Lb90
2
~Lbg0
3
|Lb90
4
~L$GDP 4 ~Lb90
.1274
-.1596
-.2220
-.1567
-.0992
.5988
.0785
.1416
-.0537
-.2607
.0314
.0824
-.2383
.0849
.3185
v
~LCurr
~LSGDP
~Lbg0
~LCurr
~LSGDP
~Lb90
RECURSIVE UNRESTRICTED SYSTEM STANDARD ERRORS
1 |LCurr 1 |LCurr 2 |LCurr 3 |L$GDP 1 |LSGDP 2
.0558
.1248
.1287
.1296
.0570
.0497
.1730
.3872
.3995
.4022
.1769
.1543
.0936
.2094
.2160
.2175
.0957
.0835
~L$GDP 4
~Lb90 1
|Lb90 2
|Lb90 3
|Lb90 4
.0323
.0802
.0770
.0818
.0744
.1002
.2489
.2390
.2537
.2310
.0542
.1346
.1293
.1372
.1249
V
~LCurr
~L$GDP
~Lb90
~LCurr
~L$GDP
~Lb90
~L$GDP
.04
.12
.07
F-tests on Retained Regressors:
F( 3, 43)
V
F =
Pr=
F =
Pr=
1
2.26
.0953
~L$GDP 3
1.00
.4036
|LCurr 1
1.92
.1406
|L$GDP 4
11.75
.0000
|LCurr 2
3.23
.0315
~Lb90 1
1.71
.1792
TABLE 6
|LCurr 3
1.67.
.1884
~Lb90 2
2.53
.0698
~LSGDP 1
.48
.6971
|Lb90 3
1.50
.2285
~L$GDP 2
.17
.9139
~Lb90 4
3.41
.0257
19
VARIABLE
|LCurr 1
ILb90 2
IL$GDP 4
COEFFICIENT
.31886
-.21442
.14122
VARIABLE
V
1
~LCurr 1
~LSGDP 1
~L$GDP 2
~L$GDP 3
|LSGDP 4
COEFFICIENT
-.42029
62820
[22261
23826
30144
56982
VARIABLE
V
1
~ LCurr 2
~Lbg0 1
COEFFICIENT
.07299
.44218
.23846
EQUATION 1 for |LCurr
STANDARD ERROR
t-RATIO
4.299
.07417
-3.251
.06595
12.061
.01171
EQUATION 2 for |L$GDP
STANDARD ERROR
.15239
.32503
.14010
.09899
.08065
.07734
EQUATION 3 for
STANDARD ERROR
.02844
.10337
.10949
TABLE 7
t-RATIO
-2.758
1.933
-1.589
-2.407
-3.738
7.368
|Lb90
t-RATIO
2.567
4.278
2.178
PROBABILIT
.0001
.0019
.0000
PROBABILIT
.0079
.0585
.1179
.0195
.0004
.0000
PROBABILIT
.0129
.0001
.0336
2O
Official and Unofficial Ouernight Cash Rates
(ueeklg means)
14.0
10.8
1988
1989
1990
Sample PePiod is 1986(Z7) - 1990(13)
Figure 1
1991
22
35OO
Figure 6
Figure 7
23
u
F
F
12.8
15.8
18.8
21.8
24.8
OFF
Figure 8
Differen%ial be%ween UnoFFicial and Official Cash Ra%ex
2.1
Figure 9
24
off
1991
SaMple Period is 1986(Z?) - 1990(13)
Figure I0
25
b90
rust
CROSS-PLOT
,
8~
,
Sample Period is 1986(27) - 198~(47)
Figure Ii
bgO-usr CROSS-PLOT
crash
Sample Period is 1986(Z?) - 1990(13)
Figure 12
26
WORKING PAPERS IN ECONOMETRICS AND APPLIED STATISTICS
~~ ~inean ~o~. Lung-Fei Lee and William E. Griffiths,
No. I - March 1979.
~utrd~ ~o~. Howard E. Doran and Rozany R. Deen, No. 2 - March 1979.
Nate on ~ Za~ ~~inan~uioc~ ~:v~on Mode!.
William Griffiths and Dan Dao, No. 3 - April 1979.
¯ o/9~. G.E. Battese and W.E. Grlfflths, No. 4 - Aprll 1979.
D.S. Prasada Rao, No. 5 - April 1979.
Ha/eao~ Req~~ad~. George E. Battese and
Bruce P. Bonyhady, No. 7 - September 1979.
Howard E. Doran and David F. Williams, No. 8 - September 1979.
D.S. Prasada Rao, No. 9 - October 1980.
~atazIian ~o2~ - 1979. W.F. Shepherd and D.S. Prasada Rao,
No. I0 - October 1980.
u~ ~o~ozu2 Neqa~cru~ia~ ~. W.E. Griffiths and
J.R. Anderson, No. II - December 1980.
and Jan Kmenta, No. 12 - April 1981.
Howard E. Doran
~i~ Oadaa d.~gw, e~2.a~k~ ~&~&#~&m%~_~.. H.E. Doran and W.E. Griffiths,
No. 13 - June 1981.
~ir~ ~eeJhb& ~ox~e RaZe. Paullne Beesley, No. 14 - July 1981.
Yo/~ Doia. George E. Battese and Wayne A. Fuller, No. 15 - February
1982.
27
D~. H.I. Tort and P.A. Cassidy, No. 16 - February 1985.
H.E. Doran, No. 17 - February 1985.
J.W.B. Guise and P.A.A. Beesley, No. 18 - February 1985.
W.E. Griffiths and K. Surekha, No. 19 - August 1985.
~~ ~. D.S. Prasada Rao, No. 20 - October 1985.
9ne-~e~ g~-~Ae ~~ed2!. William E. Griffiths,
R. Carter Hill and Peter J. Pope, No. 22 - November 1985.
William E. Griffiths, No. 23 - February 1986.
~~ ~:~mJ~on ~.~n~ ~one! Do/a: ~A ~ ~ ~
~ Daizu~ 8a~. T.J. Coelll and G.E. Battese. No. 24 February 1986.
~~ ~~ ~ ~ Dola. George E. Battese and
Sohail J. Malik, No. 25 - April 1986.
George E. Battese and Sohail J. Malik, No. 26 - April 1986.
~~. George E. Battese and Sohail J. Malik, No. 27 - May 1986.
George E. Battese, No. 28- June 1986.
~um%f.~. D.S. Prasada Rao and J. Salazar-Carrillo, No. 29 - August
1986.
W.E. Griffiths and P.A. Beesley, No. 30 - August 1987.
William E. Griffiths, No. 31 - November 1987.
H.E. Doran,
28
Chris M. Alaouze, No. 32 - September, 1988.
G.E. Battese, T.J. Coelll and T.C. Colby, No. 33- January, 1989.
8rd~to ~ ~can~-~ide~~. Colin P. Hargreaves,
No. 35 - February, 1989.
William Grlffiths and George Judge, No. 36 - February, 1989.
No. 37 - April, 1989.
Chris M. Alaouze,
~ to WO~ ~22xlCO~ ~nx~. Chris M. Alaouze, No. 38 July, 1989.
Chris M. Alaouze and Campbell R. Fitzpatrick, No. 39 - August, 1989.
Doiiu. Guang H. Wan, William E. Grlfflths and Jock R. Anderson, No. 40 September 1989.
o~ ~6xi~ ~ex~ 0~. Chris M. Alaouze, No. 41 - November,
1989.
~aq~T4o~ ~h~ (Ind~inu~ ~. William Grifflths and
Helmut L~tkepohl, No. 42 - March 1990.
Howard E. Doran, No. 43 - March 1990.
4 ~Ae Xo!/nan ~i~to @a2i~ Su~-~~. Howard E. Doran,
No. 44 - March 1990.
Howard Doran, No. 45 - May, 1990.
Howard Doran and Jan Kmenta, No. 46 - May, 1990.
~r~ ~enit~ (Ind ~a/~ ~nir.e~. D.S. Prasada Rao and
E.A. Selvanathan, No. 47 - September, 1990.
29
~con~ ~t~ ait~ %~ o~ ~eu~ ~a92~. D.M. Dancer and
H.E. Doran, No. 48 - September, 1990.
~
D.S. Prasada Rao and E.A. Selvanathan, No. 49 - November, 1990.
~p~2J~e.~2i~R4~n ~ ~~. George E. Battese,
No. 50 - May 1991.
W&m~ ~ o~ ~ ~onxa. Howard E. Doran,
No. 51 - May 1991.
Howard E. Doran, No. 52 - May 1991.
EoxnO~ ~ C.J. O’Donnell and A.D. Woodland,
No. 53 - October 1991.
Rano~ani~ fecio~. C. Hargreaves, j. Harrlngton and A.M.
Siriwardarna, No. 54 - October, 1991.
Colin Hargreaves, No. 55 - October 1991.
~ ~o ~add~ ~~ in 8nd~. G.E. Battese and T.J. Coelli,
No. 56 - November 1991.
2.0. T.J. Coelll, No. 57- October 1991.
Barbara Cornelius and Colin Hargreaves, No. 58 - October 1991.
Barbara Cornelius and Colin Hargreaves, No. 59 - October 1991.
Duangkamon Chotlkapanlch, No. 60 - October 1991.
Colin Hargreaves and Melissa Hope, No. 61 - October 1991.