Use of Money Supply in the Conduct of Japan’s Monetary Policy:
Reexamining the Time Series Evidence
September 2004

Ryuzo Miyao
Research Institute for Economics and Business Administration
Kobe University(miyao@rieb.kobe-u.ac.jp)

Abstract
Japan’s money supply and its role in monetary policy have drawn considerable
attention especially since the Bank of Japan adopted its “quantitative easing” scheme in
March 2001. This paper focuses on the role of money supply as an information variable
and reexamines the empirical relationship between money and economic activity withrecent data extending through 2003. We show that the linkage between M2+CD andincome or prices largely disappeared in the 1990s and explore possible reasons for thisbreakdown. The evidence suggests that (i) time deposits lost their predictive content for
future economic activity in the 1990s, which seems a primary reason for the breakdown
in the M2-income relationship, (ii) bank loans also became no longer useful inforecasting subsequent movements in output in the late 1990s, and (iii) there has been aclose link between time deposits and bank loans throughout the period examined. We
argue that Japan’s persistent non-performing loans problem and ongoing efforts byfirms and banks to trim excessive and inefficient bank loans may have caused thebreakdown in the bank loan-income relationship and accordingly the breakdown in theM2-income relationship by way of time deposits over the last decade.

The author is grateful to Hiroshi Fujiki and seminar participants at the Bank of Japanand JICA Hyogo for helpful comments and discussions.
11. Introduction
Japan’s money supply has drawn considerable attention over the last few years,
particularly since the Bank of Japan adopted the unprecedented “quantitative easing”
scheme in March 2001. The amount of bank reserves － more precisely, demand
deposits at the Bank of Japan － is regarded as a policy indicator and has been sharply
increased, reaching 30-35 trillion yen (i.e. excess reserves of more than 25 trillion yen)at the time of this writing. Nonetheless, the broad measure (M2+CD) in Japan has
exhibited only modest growth of around 3%. Obviously there is little linkage between
the new policy instrument and money supply. It is natural to ask if money supply still
plays a useful role in monetary policy in Japan1
The literature on monetary policy usually stresses two potential roles of money supply.
The first is its role as “an intermediate target” and the second as “an information
variable.” For money supply to be a reliable intermediate target, a stable long-run
relationship between money and policy goal variables (such as income or prices) is
required. The central bank should also be able to control money supply balances
reasonably well by implementing its policy instruments. However, several studies havealready documented that a long-run or cointegrating relationship among Japan’s M2,
output, and prices became instable or disappeared in the 1980s and early 1990s (see e.g.
Yoshida and Rasche 1990, Miyao 1996, Tsukuda and Miyakoshi 1998). Lack of themoney supply controllability should be apparent in light of these recent conclusions.2
What about the second role of money supply as an information variable? It seems
reasonable to use money supply as a guide to conduct monetary policy when money
supply has the predictive content for subsequent movements in income or prices. This
analysis on short-run forecastability is also known as the Granger causality (ormoney-income causality) and a number of studies have investigated this issue for thecase of Japan. Researchers such as Honda et al (1995) and Tsukuda and Miyakoshi

1 Hereafter we use the term “money supply” to mean the broad money supply (M2+CD)

rather than a narrow measure of money supply (M1). Note also that M2 and M2+CD areused interchangeably.
2 The Bank of Japan has announced the “money supply forecast” since the late 1970s
but it has never been used formally as an intermediate target (see e.g. Okina 1993). Ito(1989) showed that the behavior of M2 forecasts had violated a rigid monetarist rule forthe period 1978-1988.
2

(1998) claimed that money-to-income causality weakened or almost disappeared in the
1980s. Morimune and Zhao (1997) found the causality from nominal income to money
but not vise versa for the period of 1960-1990. Following the work of Feldstein andStock (1994), Ikeno (2001) reexamined the evidence in a more systematic way andargued that a stable causal relationship from M2 to nominal GDP growth may exist
even in the 1980s. More recently, the Bank of Japan (2003, Chart 15) showed evidence ofGranger causality suggesting that the predictability of M2 disappeared in the 1990s.
The existing empirical evidence appears mixed in Japan. But more importantly,
detailed investigations for the late 1990s are missing in the literature. We need toclosely study developments in these recent years because they have potential
implications for the conduct of monetary policy in Japan especially under the new policyof zero interest rates and quantitative easing.
Motivated by these observations, this paper focuses on the role of money supply as
information variables and reexamines evidence on the short-run money-income
relationship in Japan for the period of 1975-2003. In Section 2, we investigate three
forecasting models, i.e. forecasting nominal GDP growth, real GDP growth, and GDP
deflator inflation with several different sets of regressors. Time-series properties (i.e.
unit root and cointegration) of the variables are also carefully examined.
To anticipate the main findings here, we show that the empirical relationshipbetween M2 and income or prices largely disappeared in the 1990s. The result seems
reasonably robust as it is consistently shown with several different model specifications.
We then explore possible reasons for this breakdown in Section 3. Dividing M2+CD into
two series (M1 and time deposits plus CD), we investigate the predictive contents ofthese series. We further study the short-run forecastability of bank loans as it may give
us additional insight. The evidence suggests that (i) time deposits lost the predictive
content for subsequent economic activities in the 1990s, which seems a primary reason
for the breakdown in the M2-income output relationship, (ii) bank loans also became nolonger useful in forecasting future economic activity in the 1990s, and (iii) there has
been a close link between time deposits and bank loans throughout the period examined.
We argue that Japan’s persistent non-performing loans problem and ongoing efforts byfirms and banks to trim excessive and inefficient bank loan balances may have caused
the breakdown in the bank loan-income relationship and accordingly the breakdown in3the M2-income relationship through time deposits over the last decade.

2. Reexamining Predictive Content of M2 in Japan
This section reexamines the evidence on the predictive content of M2 in Japan. We
address this issue by using updated, quarterly data on money, output, prices, andinterest rates for the period of 1975:1-2003:4.
The following variables are used in our analysis: logged M2 (LM2), logged nominal
GDP (LY), logged GDP deflator (LP), logged real GDP (LY95), call rate (RCALL), andlogged call rate (LRCALL).3
As a preliminary step, we perform unit root tests for each of the variables. We run theaugmented Dickey Fuller (1979) tests of a unit root against no unit root (denoted asADF), and a modified Dickey-Fuller test based on GLS (Generalized Least Squares)
detrending (denoted as DF-GLS), a powerful univariate test proposed by Elliot,
Rothenberg, and Stock (1996). A constant and a time trend are included for variables inlevels (i.e. detrended test) except for the call rate series. A constant is included for thecall rate series in levels and the variables in first differences (i.e. demeaned test). In allthese tests, the optimal lag length is chosen based on the Schwarz BayesianInformation Criterion (SBIC) up to eight lags.
As shown in Panel A of Table 1, no test rejects the null of a unit root against thealternative of stationarity. Taking the first difference, both tests detect strong rejections
for almost all the cases (Panel B). In particular, powerful DF-GLS tests result inrejections for all the variables. These results indicate that each of the variables can betreated as a single unit root process or integrated of order one (I(1)). They also imply

M2 is the seasonally adjusted, monthly average series taken from the Nikkei
Database (code: MNQMACD@) and monthly observations are averaged within eachquarter to obtain quarterly series. Nominal GDP, real GDP, and GDP deflators areseasonally adjusted and retrieved from 93SNA. Because 93SNA data are available from1980, they are linked with corresponding 68SNA data at 1980:1. These SNA statistics
can be taken from the Cabinet Office of Japan’s website atwww.esri.cao.go.jp/en/sna/menu.html. The call rate series is constructed first by linking
the uncollateralized overnight rate (monthly average) after July 1985 and thecollateralized rate (monthly average) until June 1985, and then taking the average ofmonthly observations in each quarter. In linking the two series, the mean difference
between the two is added to the collateralized rate. These call rate data are taken fromthe statistics section of the Bank of Japan’s website (www.boj.or.jp).

34

that we may take the first differences for each variable in the short-run forecasting
analysis below.
Next we proceed to the cointegration analysis and examine whether there exists a
long-run cointegrating relationship among money, income, and prices or interest rates.
The following systems are considered: (LM2, LY), (LM2, LP, LY95), (LM2, LY, RCALL),and (LM2, LY, LRCALL). Here two conventional cointegration tests are performed: theaugmented Dickey-Fuller test of no cointegration against cointegration (denoted ADF
again) and Johansen’s (1988) and Johansen and Juselius’s (1990) maximal eigen value
test of no co-integration against one cointegrating vector (JOH). All the tests assume
that some regressors in the system contain a time trend. The lag length for the ADF test
is chosen based on the SBIC. Four lags are arbitrarily used for JOH.4 Critical values
are tabulated by Fuller (1976) and Phillips and Ouliaris (1990) for ADF andOsterwald-Lenum (1992) for JOH. As for JOH, following the procedure by Cheung andLai (1993), Osterwald-Lenum’s critical values are corrected to account for possible size
distortions in finite samples.
Table 2 reports the cointegration test results. We cannot reject the null of no
cointegration from either of these tests. The evidence of no cointegration is consistent
with earlier results such as Miyao (1996) and Tsukuda and Miyakoshi (1998).
Accordingly, a stable long-run relationship among money, output, and prices is notsupported by our updated data. 5 It also implies that the short-run forecasting
regressions below do not have to include an error-correction term.
We now examine the predictive content of M2 in the forecasting regressions.
Dependent variables are LY95, LP, and LY, all in first differences. The sets of regressors

Using the SBIC, we consistently select one lag, which appears too short to analyze
dynamic interactions of the economy. We employ the likelihood ratio tests for the vector
autoregressive systems, such as one vs. two lags, two vs. four lags, four vs. six lags oreight lags, and select four or six lags. We consider four lags as our benchmark. Note thattest results are unaffected using six lags.
5 Other evidence includes Morimune and Zhao (1997) and Ikeno (2001) where more orless mixed results are reported. We also allow for a possible break in cointegration
employing procedures by Gregory and Hansen (1996). We implement the augmented
Dickey-Fuller test of no cointegration against cointegration with a structural shift in
the cointegrating vector (i.e. a break in the intercept term only, or in both the intercept
and slope coefficients) in an unknown timing. But again the null of no cointegration is
not rejected with any of these models.
4

5

for forecasting LY95 and LP are(LY95, LP, LM2)
(LY95, LP, LM2, RCALL)(LY95, LP, LM2, LRCALL)
and those for forecasting LY are(LY, LM2)(LY, LM2, LP)(LY, LP, LM2, LRCALL)(LY, LP, LM2)
(LY, LP, LM2, RCALL)(LY, LP, LM2, LRCALL)
all of which enter in first differences. F tests are performed to examine whether the
restriction on coefficients on M2 growth are zero. Four lags and a constant term areincluded in the system. We also consider two lags. Sample periods are the full sample(1975:1-2003:4) and two subsamples (1975:1-1992:4 and 1993:1 -2003:4).6
Table 3 reports the test results. These F statistics indicate that the restriction of zerocoefficients is strongly rejected for all the models in the full sample and the 1975-92
subsample except for the cases of LP equation with four lags. On the contrary, M2 is nolonger statistically significant for almost all the cases with the subsample after 1993
and the result looks quite robust. These empirical results suggest that the short-run
predictive content of M2 indeed disappeared in the 1990s.7
Visual inspection of the actual data may also be helpful to confirm these findings.
Figure 1 displays M2 growth and output growth (changes from the previous year) for
1976-2003. Positive associations between money growth and income growth (both realand nominal) are observed until the early 1990s, but afterwards these two series started

Note that the M2 growth rate (changes from the previous year) turned negative in late1992, which motivates our selection of these subsamples. We also use six lags but theresults are quite similar to the four-lag case.
7 We also employ Toda and Yamamoto’s (1995) causality analysis as an alternative
testing procedure, in which all the variables enter in levels with a constant, a time trend,
and five lags of explanatory variables (thus we assume the true model contains four lags
and the maximum order of integration is one). Using this procedure, we find somewhat
weaker rejections in the pre-1993 subsample (mostly with LP equations), but again
these rejections disappear with the post-1993 subsample.
66

to move independently. The formal econometric analysis, controlling for other factors,
indeed supports this observation.

3. Why Did the Predictive
redictive Content of M2 Disappear in the 1990s?
Why did M2 lose the predictive content for future output or prices in the 1990s? To
answer that question, we may need to identify which component of M2 lost thepredictability. Specifically, we divide M2+CD into two series: M1 and time deposits plus
CD, both in log (denoted as LM1 and LTDCD, respectively).8 The same causality tests
are performed for the models that now include LM1 or LTDCD instead of LM2.
Table 4 shows the results on the predictive content of LM1.9 Clearly M1 is notstatistically significant in predicting output or prices for any sample period, even withthe former 1992 subsample. Because the M1 component had little predictive content
even in the pre-1993 period, the insignificance of M1 may not be a major factor to
explain the breakdown in the M2-income relationship in the 1990s.
Table 5 demonstrates the causality results with LTDCD. Time deposits plus CD had
the predictive content for the pre-1993 period, and it disappeared after 1993. These arevirtually the same results as the M2 case (Table 3). The evidence suggests that the
behavior of time deposits, rather than the M1 component, is a primary reason for thediminishing role of M2.
Then a further question arises: Why did LTDCD lose the predictability in the 1990s?
It has been pointed out that M2 behavior is largely linked with movements in bankloans. If this is the case, then examining the predictive content of bank loans may be
useful to gain some insight. To this end, we once again perform the same forecasting
exercises with the regression models that contain logged bank loans (LBL) instead of

M1, time deposits (quasi money), and CD (certificates of deposits) are seasonallyadjusted, monthly average series taken from the Nikkei Database (code: MONEYA@,MDTA@, and MNCDA@). For each series, monthly observations are averaged withineach quarter to obtain quarterly series.
9 As for M1, a stable cointegrating money demand relationship may exist when thelogged call rate is used (see Miyao 2002 and Nakashima and Saito 2002). In this case,an error-correction term needs to be included in the equations. However, ourinvestigation reveals that the long-run M1 demand residual has only limited marginal
predictive power in the case of Japan.
87

M2.10
Table 6 provides test results for LBL. It conveys the same impression as before,
namely the bank loans series is statistically significant in most cases for the pre-1993
sample, and loses the predictive content after 1993. Therefore, bank loans no longer
contain useful information for subsequent economic activity in the 1990s.
One can observe a strong link between bank loans and time deposits by looking at thetime series data. Figure 2 displays the time series data of bank loans, time deposits plus
CD, and demand deposits, all in levels. Bank loans and time deposits move very closely
for almost all of our sample period. There is a noticeable jump in the bank loan series in1993 due to a definitional change (see footnote 9), but if that effect is subtracted fromthe data, high growth until the late 1980s and subsequent stagnation in the 1990s
correspond with each other. On the other hand, the behavior of demand deposits
appears independent of movements in bank loans and shows persistent increases rather
than stagnation in the late 1990s. This can be attributed at least partly to increases inprecautionary money demand during the time of Japan’s financial crisis. 11 Theseobservations suggest that the textbook explanation of deposit creation, in whichchanges in bank loans correspond to changes in deposits in the form of derivative
deposits, has been particularly relevant for time deposits rather than demand deposits
in Japan. While this fact is yet to be explained theoretically, the strong association
observed in the actual time series is indeed suggestive to establish the linkage.12

The bank loans series is total loans and discounts outstanding by domesticallylicensed banks. The series is retrieved from the statistics section of the Bank of Japan’s
website and is seasonally adjusted by X11. Note that this series contains a definitionalchange at 1993:2 when overdrafts and cash advances are additionally included in thedata. This effect is taken into account in the causality analysis here by using acorresponding dummy variable (which is set to one at 1993:2 and zero otherwise).
11 Notice also that in April 2002, the pay off for time deposits started in Japan andconsequently a substantial amount of time deposits shifted to demand deposits and/or
cash currency.
12 This theory on “deposit creation” may be more relevant for time deposits held byprivate corporations (denoted as TDP) rather than those by individuals (TDI) as thelatter may be more or less viewed as primitive liabilities for banks. To check thispossibility, we similarly investigate predictive contents of TDP and TDI series, both inlog, with four lags. These series (end-of-month observations) are taken from the Nikkei
database and transformed to quarterly observations (and also seasonally adjusted by
X11). The results indicate that using TDP, somewhat weaker but similar rejections aredetected with the pre-1993 subsample and again those rejections disappear with the108Taking into account all this evidence, we may be able to identify bank loan behavior
as the primary reason for the breakdown in the causal relationship between M2 andeconomic activity in the 1990s. There is little doubt that the presence of severe badloans accumulated in the post-bubble period significantly diminished its forecastability.
Both banks and firms have tried to trim inefficient and excessive loans outstanding
from the late 1990s after the banking-sector problem was finally disclosed to the public.
Japan had to maintain these efforts to correct the excess supply structure of the whole
economy no matter what economic conditions were expected in the near future. As aresult, bank loans lost the short-run predictive content for future economic activity. In
Figure 3, the time series data of bank loan growth and real GDP growth (changes fromthe previous year) are plotted. It is also evident from this figure that bank loans havebeen declining and therefore have lost a close empirical relationship with real GDP
since the mid-1990s. And this would lead to the corresponding breakdown in the
short-run M2-income relationship by way of the behavior of equally stagnant time
deposits.
Our empirical investigation suggests that Japan’s broad money supply no longer hasa reliable empirical relationship with output or prices from the perspectives of short-run
forecasting or information variables. Recall also that the evidence does not support a
stable relationship with long-run, cointegration perspectives. Accordingly, the use ofmoney supply in carrying out monetary policy may remain limited until these broken
relationships are adequetly restored.

4. Discussions
In this section we discuss several issues related to our main findings on the predictive
content of money supply in Japan.

4.1 Predictive content of other financial variables

post-1993 subsample. As for TDI, virtually no (i.e. only one) rejection is found before
1993 and no rejection is found after 1993. Thus one may conclude that time deposits
held by private corporations are most responsible for the breakdown in the M2-income
relationship in the late 1990s and this actually lends support to our “deposit creation”
theory that tries to connect time deposits and bank loans. I am indebted to Hiroshi
Fujiki for suggesting this possibility.
9First, we examine whether any other financial variables may possess predictive
power even in the 1990s. For this exercise, we employ the series of monetary base, stock
prices, and foreign exchange rates, all in logarithm and in first differences.13 Monetarybase is another measure of monetary aggregates, which may be more controllable thanbroader aggregates such as M1 or M2.14 Stock prices and exchange rates are typicallyknown as “forward looking” variables as economic theories usually predict that they are
determined by the public expectation of future economic variables. We follow the same
exercises as above to study the role of these financial variables as information variables.
Tables A-1, A-2, and A-3 in the appendix indicate the results. As shown in Table A-1,
the monetary base largely had the predictive content for the pre-1993 period, but it
disappeared after 1993. Stock prices are found to be generally significant for the
pre-1993 and full samples, but again, rejections are no longer detected with thepost-1993 period (see Table A-2). As seen in Table A-3, the exchange rate series has
virtually no marginal predictive power for future economic activity in any of the sample
periods.15 Thus, the role of the three financial variables considered here seems limitedin the 1990s.

4.2 Predictive content of interest rates and the term structure
Second, we further examine the predictive content of interest rate variables (RCALL,LRCALL) and the term structure of interest rates (i.e. the spread between long-termand short-term interest rates, denoted as SPRD).16 There is a large body of literature in

The monetary base series is monthly average, seasonally adjusted by X12-ARIMA
and taken from the Bank of Japan’s website. Note that the effects of changes in thereserve requirement rate are adjusted in this series. The stock price series is the Nikkei
Stock Average (225 selected stocks in the Tokyo Stock Exchange), monthly average, andis taken from the Nikkei Database (code: JSRSPA). The exchange rate series is the
yen-dollar spot rate, monthly average, and is taken from the Nikkei Database (code:
REXDA). Monthly observations for each series are averaged within each quarter toobtain quarterly series. Unit root properties for these series are checked by ADF and
DF-GLS tests and each of them is shown to be I(1).
14 Honda et al (1995) stressed that the monetary base (reserve adjusted) has superior
predictive power to M2 in the 1980s.
15 The Japanese tend to overreact to movements in exchange rates, but too muchconcern with exchange rate behavior (especially yen appreciation) may not be
warranted from short-run forecasting perspectives.
16 The interest rate spread series is the 10-year government bond rate minus the
3-month Gensaki rate, retrieved from the Nikkei Database (code: BYR3AV and13

10

the U.S. investigating whether interest rates and/or spread variables have superior
predictive value over monetary aggregates (see e.g. Stock and Watson 1989, Bernanke
and Blinder 1992, Friedman and Kuttner 1992). We check the significance of RCALL
and LRCALL in our main analysis using M2. The role of the spread series is also
examined by augmenting SPRD in the benchmark M2 models.
Results are reported in Tables A-4 and A-5. In Table A-4, we find several rejections interms of RCALL and LRCALL for the pre-1993 and full sample periods. Yet, comparedwith Table 3, they have less predictive content than M2. In some models LRCALL isfound (weakly) significant with the latter subsample. This may indicate that since thelate 1990s when interest rates dropped to very low or near zero levels, specifying models
with logged interest rates provide more useful information in terms of forecasting.17
Table A-5 shows that the spread variable may have marginal predictive value in thelate 1990s, especially in forecasting LY (nominal GDP). Note that several studies
examined the role of the term structure spread in predicting future output or recessions
in Japan (e.g. Kim and Limpaphayom 1997, Hirata and Ueda 1998, Ikeno 2003,
Nakaota 2003). Using different approaches from ours, most of the studies found the
spread largely significant.18 The interest rate spread appears to be the most promisingpredictor after the late 1990s of all the variables considered here. Nevertheless, ourresults indicate only partial significance and further scrutinies are warranted.

4.3 Stability tests
Third, we conduct several formal tests of parameter stability in our forecasting
equations. Given that M2 lost its predictive content in the 1990s, there may be a breakin the parameters of these forecasting equations. Following the approach by Feldsteinand Stock (1994) and Ikeno (2001), we adopt the CUSUM-type test proposed by
Ploberger and Kramer (1992), the exponential average LM test (Exp-LM) proposed byAndrews and Ploberger (1994), and conventional Chow tests. The regressions include
four lags for all these tests. The CUSUM-type test rejects the stability if the model
LBBLTYNKM).
17 Not to mention, interest rates in log move to a larger extent under the near zeroenvironment than interest rates in levels.
18 Among the studies cited here, Ikeno (2003) provided the most cautious assessment on
the predictive role of the interest rate spread.
11

systematically over- or under-forecasts the dependent variable. Exp-LM procedures,
which are known to be most powerful against distant local alternatives, test the null ofparameter constancy against the alternative that regression coefficients shift at an
unknown date. The first and last 15% of observations of the total sample are trimmed inthis procedure. As to conventional Chow tests, we test a possible shift in the parameters
at given break dates: 1995:3 and 1997:4 (denoted as CHOW95 and CHOW97,
respectively). These dates correspond to episodes of major bank failures in Japan.19
These banking crisis episodes may have possibly influenced the M2-income relationshipby way of a shift in the bank loans behavior on both lending and borrowing sides.
Table A-6 reports stability test results. According to the CUSUM-type test, we cannot
reject the null of stability. This indicates that one-step-ahead forecast errors, which arecomputed by the regression models as a whole, do not become too large in absolutevalues even in the 1990s. Thus the models may not be so instable from the in-sample
forecast error perspective. On the other hand, Exp-LM and Chow tests reject thestability in most of the model specifications. Note that these test statistics are allcomputed based on F statistics and therefore they have more direct relevance with our
main analysis. Taking these results and considerations together, we view that the test
results generally lend support to a break in the regression coefficients in the late 1990s.

4.4 Further comments on the reasons for a breakdown in the M2-income relationship
Finally, we discuss other possible reasons for a breakdown in the M2-income
relationship. Having a similar motivation as ours, the Bank of Japan (2003) studied therole of money supply in recent years and gave two explanations why the long-run stable
relationship between M2 and economic activity broke down in the late 1990s. The first
explanation is that a large increase in precautionary money demand took place due tothe reductions in excess liabilities of firms and the non-performing loan problems of the
1990s. The second is that a large amount of funds shifted from riskier financial assets
into deposits, partly due to problems with the financial system. Thus the Bank of Japan

In August 1995, the largest credit union (Kizu Credit Union) and the largest
secondary regional bank (Hyogo Bank) failed simultaneously. In November 1997,
Yamaichi Securities and Hokkaido Takushoku Bank consecutively failed, which spurreda sense of financial crisis in Japan.
19

12

pointed out similar underlying causes, but with different channels.
Note that these explanations may be valid as potential reasons from the perspectiveof a long-run cointegrating relationship. But, from the short-run forecasting perspective,
we have stressed that the channel from bank loans to time deposits is more important
than channels originated in M1 components. Because large increases in precautionary
money demand or safer deposits mainly appear in M1 components (such as demand
deposits) and time deposits remain stagnant through the 1990s, these channels may beof secondary importance from our forecasting perspective.

5. Concluding Remarks
This paper has reexamined the evidence on Japan’s money-income causality for theperiod of 1975-2003 to see if a broad money supply (M2+CD) still plays its role as an
information variable in the conduct of monetary policy. Our systematic investigation
with several different specifications showed that M2 no longer has the predictive
content for future economic activity from the late 1990s.
We further explored possible reasons for this breakdown by providing the following
evidence: (i) time deposits lost the predictive content in the 1990s, which seems aprimary reason for the breakdown in the M2-income relationship, (ii) bank loans also
became no longer useful in forecasting future economic activity in the 1990s, and (iii)
there has been a close link between time deposits and bank loans throughout the period
examined. We argued that Japan’s persistent non-performing loans problem andongoing efforts by firms and banks to trim excessive and inefficient outstanding bankloans can be a chief cause of the breakdown in the bank loan-income relationship, which
in turn would lead to the breakdown in the M2-income relationship by way of equallystagnant behavior of time deposits.
In sum, our empirical investigations consistently suggest that Japan’s broad money
supply no longer has a reliable empirical relationship with output or prices fromshort-run forecasting or information variables perspectives. Recall also that theevidence does not support the presence of a stable long-run relationship fromcointegration analysis, either. Accordingly, the use of money supply in the conduct ofmonetary policy may remain limited until these broken relationships are satisfactorily
restored. It is highly important to look for other information variables that maintain a13

robust empirical relationship with economic activity even in the recent years offinancial instability and near zero interest rates. A preliminary investigation in thispaper indicates that the interest rate spread may be a potentially important candidate,
which deserves further scrutiny.

14

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16

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17

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18

Table 1. Unit Root Test Results——————————————————————————————————————
Variable

ADF

DF-GLS——————————————————————————————————————

A. In levels
LM2-1.09(1)-0.42(1)LY-1.14(0)-0.79(3)LP-1.97(2)-0.36(3)LY95-0.29(0)-1.23(3)RCALL-2.10(1)-0.06(3)

LRCALL

3.67(5)4.38(5)

B. In first differences
ΔLM2-2.29(0)-2.38(0)*ΔLY-2.25(3)-2.32(2)*ΔLP-2.63(2)†-2.47(2)*ΔLY95-8.44(0)**-3.62(2)**ΔRCALL

-6.39(0)**-5.58(2)**ΔLRCALL-4.82(5)**-4.51(5)**——————————————————————————————————————
Notes: This table reports statistics testing for a unit root for logged M2 (LM2), logged
nominal GDP (LY), logged GDP deflator (LP), logged real GDP (LY95), call rate
(RCALL), and logged call rate (LRCALL). ADF is the augmented Dickey-Fuller(1979) test of a unit root against no unit root, and DF-GLS is a Dickey-Fuller test
based on GLS-detrended series, proposed by Elliott, Rothenberg, and Stock (1996). A
constant and a time trend are included for variables in levels except for the call rateseries (i.e. detrended test). A constant is included for the call rate series in levels andvariables in first differences (i.e. demeaned test). In all these tests, the optimal lag
length is chosen based on the SBIC and is shown in parentheses. The sample period
is 1975:1-2003:4. Critical values, tabulated by Fuller (1976), Elliott, Rothenberg, andStock (1996) are:
Detrended
Demeaned

10%(†)

5%(*)

1%(**)

ADF

-3.15

-3.45

-4.40

DF-GLS

-2.74

-3.03

-3.58

ADF

-2.58

-2.89

-3.51

DF-GLS

-1.61

-1.95

-2.60

19

Table 2. Cointegration Test Results——————————————————————————————————————
SystemADF

JOH——————————————————————————————————————(LM2, LY)

0.11(0)6.50(LM2, LP, LY95)-2.02(3)10.96(LM2, LY, RCALL)-1.40(1)

16.53(LM2, LY, LRCALL)-2.60(0)20.38——————————————————————————————————————
Notes: This table reports statistics testing for cointegration in the systems listed in
the first column. ADF is the augmented Dickey-Fuller (1979) test of no cointegration
against cointegration. JOH is Johansen’s maximal eigenvalue test of no
cointegration against one cointegrating vector. All the tests assume that somevariables in the system contain a time trend. The lag length for ADF test is chosen
based on the SBIC and is shown in parentheses. Four lags are arbitrarily used forJOH. Critical values are tabulated by Fuller (1976) and Phillips and Ouliaris (1990)
for ADF, and Osterwald-Lenum (1992) for JOH. As for JOH, following the procedureby Cheung and Lai (1993), Osterwald-Lenum’s critical values are corrected toaccount for possible size distortions in finite samples.
Two-variable
Three-variable

10%(†)

5%(*)

1%(**)

ADF

-3.15

-3.45

-4.40

JOH

12.91

15.07

19.95

ADF

-3.52

-3.80

-4.36

JOH

20.62

23.30

28.36

20

Table 3. Predictive Content of M2——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————
A. Predicting LY95
<LAG = 2>(LY95, LP, LM)
5.516(0.005)**
6.113(0.004)**
0.556(0.578)(LY95, LP, LM, RCALL)
5.924(0.004)**
6.784(0.002)**0.457(0.637)(LY95, LP, LM, LRCALL)
6.158(0.003)**
6.171(0.004)**
0.618(0.545)
B. Predicting LP(LY95, LP, LM)
4.044(0.020)*
4.322(0.017)*
2.151(0.131)(LY95, LP, LM, RCALL)
3.589(0.031)*
4.063(0.022)*
1.395(0.261)(LY95, LP, LM, LRCALL)
4.382(0.015)*
4.088(0.022)*
1.969(0.155)
C. Predicting LY(LY, LM)
11.943(0.000)**
9.684(0.000)**
1.913(0.161)(LY, LP, LM)
11.180(0.000)**
14.055(0.000)**
2.757(0.077) †(LY, LM, RCALL)
12.442(0.000)**
10.611(0.000)**
1.451(0.247)(LY, LM, LRCALL)
12.442(0.000)**
10.197(0.000)**
1.744(0.189)(LY, LM, RCALL, LP)
11.468(0.000)**
15.375(0.000)**
2.027(0.147)(LY, LM, LRCALL, LP)
12.738(0.000)**
14.509(0.000)**
2.916(0.067) †A. Predicting LY95
2.293(0.065)†
2.603(0.046)*
0.716(0.587)
2.575(0.043)*
3.038(0.026)*
1.083(0.385)
2.009(0.100)
2.826(0.034)*
0.973(0.439)
B. Predicting LP(LY95, LP, LM)
1.524(0.201)
1.334(0.269)
1.187(0.336)(LY95, LP, LM, RCALL)
0.667(0.617)
0.574(0.683)
0.724(0.583)(LY95, LP, LM, LRCALL)
1.689(0.159)
0.945(0.446)
1.381(0.267)
C. Predicting LY(LY, LM)
3.941(0.005)**
3.806(0.008)**0.939(0.453)(LY, LP, LM)
3.959(0.005)**
5.099(0.001)**
2.121(0.102)(LY, LM, RCALL)
3.655(0.008)**
3.451(0.014)*
1.006(0.420)(LY, LM, LRCALL)
3.591(0.009)**
3.863(0.008)**
1.091(0.378)(LY, LM, RCALL, LP)
3.483(0.011)*
4.497(0.004)**
2.299(0.085) †(LY, LM, LRCALL, LP)
3.752(0.007)**4.762(0.002)**2.997(0.036)*——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on M2
growth are zero. The regressions include two or four lags of variables listed in the firstcolumn, all in first differences. Dependent variables in first differences are indicated at
the top of each panel. P-values are reported in parentheses. **, *, and † indicate that the
restriction is rejected by the 1%, 5%, and 10% significance levels, respectively.
<LAG = 4>(LY95, LP, LM)(LY95, LP, LM, RCALL)(LY95, LP, LM, LRCALL)

21

Table 4. Predictive Content of M1——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————
A. Predicting LY95
<LAG = 2>(LY95, LP, LM)
0.302(0.740)
0.419(0.660)
0.044(0.957)(LY95, LP, LM, RCALL)
0.408(0.666)
0.778(0.464)
0.035(0.966)(LY95, LP, LM, LRCALL)
0.432(0.651)
0.551(0.579)
0.145(0.865)
B. Predicting LP(LY95, LP, LM)
0.667(0.516)
0.996(0.376)
1.339(0.274)(LY95, LP, LM, RCALL)
0.303(0.739)
0.842(0.436)
1.351(0.272)(LY95, LP, LM, LRCALL) 0.533(0.588)
1.345(0.269)
1.342(0.274)
C. Predicting LY(LY, LM)
0.018(0.982)
0.289(0.750)
0.061(0.941)(LY, LP, LM)
0.168(0.845)
0.891(0.416)
0.378(0.688)(LY, LM, RCALL)
0.083(0.920)
0.956(0.391)
0.023(0.977)(LY, LM, LRCALL)
0.086(0.918)
0.826(0.443)
0.619(0.544)(LY, LM, RCALL, LP)
0.145(0.865)
1.641(0.203)
0.409(0.667)(LY, LM, LRCALL, LP)
0.162(0.851)
1.613(0.209)
0.326(0.724)

A. Predicting LY95
0.208(0.934)
0.261(0.901)
0.175(0.950)
0.294(0.881)
0.507(0.731)
0.224(0.923)
0.243(0.913)
0.491(0.742)
0.521(0.721)
B. Predicting LP(LY95, LP, LM)
0.583(0.676)
1.113(0.361)
0.201(0.936)(LY95, LP, LM, RCALL)
0.453(0.770)
1.175(0.335)
0.113(0.977)(LY95, LP, LM, LRCALL) 0.326(0.860)
1.985(0.114)
0.370(0.828)
C. Predicting LY(LY, LM)
0.496(0.738)
1.049(0.391)
0.095(0.983)(LY, LP, LM)
0.391(0.815)
1.277(0.292)
0.169(0.953)(LY, LM, RCALL)
0.741(0.566)
1.528(0.209)
0.269(0.896)(LY, LM, LRCALL)
0.496(0.739)
1.895(0.127)
1.086(0.381)(LY, LM, RCALL, LP)
0.359(0.837)
1.871(0.133)
0.200(0.936)(LY, LM, LRCALL, LP)
0.204(0.936)
2.262(0.078)†
0.959(0.446)——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on M1
growth are zero. The regressions include two or four lags of variables listed in the firstcolumn, all in first differences. Dependent variables in first differences are indicated at
the top of each panel. P-values are reported in parentheses. **, *, and † indicate that the
restriction is rejected by the 1%, 5%, and 10% significance levels, respectively.
＜LAG = 4＞(LY95, LP, LM)(LY95, LP, LM, RCALL)(LY95, LP, LM, LRCALL)

22

Table 5. Predictive Content of Time Deposits and CD——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————＜LAG = 2＞
A. Predicting LY95
(LY95, LP, LM)
1.864(0.160)
5.726(0.006)**
0.095(0.910)(LY95, LP, LM, RCALL)
1.882(0.158)
5.799(0.005)**
0.073(0.930)
6.031(0.004)**
0.114(0.892)(LY95, LP, LM, LRCALL)
2.859(0.062)†
B. Predicting LP(LY95, LP, LM)
2.383(0.098)†
1.171(0.318)
0.901(0.415)(LY95, LP, LM, RCALL)
2.370(0.099)†
0.446(0.642)
0.906(0.413)(LY95, LP, LM, LRCALL)
2.003(0.140)
0.335(0.717)
0.769(0.471)
C. Predicting LY(LY, LM)
6.135(0.003)**8.536(0.001)**
0.259(0.773)(LY, LP, LM)
4.675(0.011)*
9.147(0.000)**
0.160(0.852)(LY, LM, RCALL)
5.887(0.004)**
7.045(0.002)**0.232(0.794)(LY, LM, LRCALL)
7.512(0.001)**
6.837(0.002)**
0.866(0.429)(LY, LM, RCALL, LP)
4.641(0.012)*
7.564(0.001)**
0.153(0.858)(LY, LM, LRCALL, LP)
5.704(0.005)**
7.403(0.001)**
0.119(0.888)
A. Predicting LY95
0.701(0.594)
2.431(0.061)*
0.108(0.979)
0.653(0.626)
2.599(0.049)*
0.062(0.992)
1.923(0.114)
3.093(0.025)*
0.263(0.899)
B. Predicting LP(LY95, LP, LM)
0.562(0.691)
0.843(0.505)
0.241(0.913)(LY95, LP, LM, RCALL)
0.456(0.767)
0.398(0.809)
0.182(0.946)(LY95, LP, LM, LRCALL)
0.506(0.732)
0.413(0.798)
0.437(0.781)
C. Predicting LY(LY, LM)1.469(0.218)
4.418(0.004)**
0.389(0.815)(LY, LP, LM)
1.357(0.255)
4.959(0.002)**0.124(0.973)(LY, LM, RCALL)
1.289(0.280)
3.404(0.016)*
0.436(0.782)(LY, LM, LRCALL)
3.114(0.019)*
3.767(0.010)**1.101(0.374)(LY, LM, RCALL, LP)
1.164(0.332)
3.879(0.009)**
0.113(0.977)(LY, LM, LRCALL, LP)
2.869(0.028)*
4.256(0.005)**
0.478(0.751)——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on time
deposits plus CD growth are zero. The regressions include two or four lags ofvariables listed in the first column, all in first differences Dependent variables infirst differences are indicated at the top of each panel. P-values are reported inparentheses. **, *, and † indicate that the restriction is rejected by the 1%, 5%, and
10% significance levels, respectively.
<LAG = 4>(LY95, LP, LM)(LY95, LP, LM, RCALL)(LY95, LP, LM, LRCALL)

23

Table 6. Predictive Content of Bank Loans——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————＜LAG = 2＞
A. Predicting LY95
(LY95, LP, LM)
0.341(0.712)
2.448(0.096)†
0.446(0.644)(LY95, LP, LM, RCALL)
0.333(0.718)
2.476(0.094)†
0.849(0.437)(LY95, LP, LM, LRCALL)
0.261(0.771)
2.464(0.095)†
0.655(0.526)
B. Predicting LP(LY95, LP, LM)
1.656(0.196)
1.053(0.356)
1.225(0.306)(LY95, LP, LM, RCALL)
2.586(0.081)†
0.690(0.506)
0.808(0.454)(LY95, LP, LM, LRCALL)
1.628(0.202)
0.761(0.472)
1.213(0.310)
C. Predicting LY(LY, LM)
2.081(0.130)
2.583(0.084)†
0.006(0.994)(LY, LP, LM)
1.336(0.268)
3.922(0.026)*
0.040(0.961)(LY, LM, RCALL)
2.435(0.093)†
2.436(0.097)†
0.217(0.806)(LY, LM, LRCALL)
1.999(0.141)
2.394(0.101)
0.001(0.999)(LY, LM, RCALL, LP)
1.643(0.199)
3.447(0.039)*
0.292(0.749)(LY, LM, LRCALL, LP)
1.180(0.312)
3.474(0.038)*
0.038(0.963)
A. Predicting LY95
1.116(0.354)
1.943(0.119)
0.746(0.568)
0.979(0.424)
2.082(0.100)
0.368(0.829)
1.310(0.273)
2.303(0.074)†
1.079(0.387)
B. Predicting LP(LY95, LP, LM)
0.475(0.754)
0.527(0.716)
0.727(0.581)(LY95, LP, LM, RCALL)
0.734(0.571)
0.224(0.924)
0.336(0.851)(LY95, LP, LM, LRCALL)
0.517(0.723)
0.262(0.901)
0.874(0.493)
C. Predicting LY(LY, LM)
1.966(0.106)
2.201(0.082)†
0.798(0.535)(LY, LP, LM)
1.512(0.205)
3.099(0.024)*
0.643(0.636)(LY, LM, RCALL)
2.016(0.099)†
2.092(0.097)†
0.638(0.639)(LY, LM, LRCALL)
2.150(0.081)†
2.174(0.086)†
1.352(0.274)(LY, LM, RCALL, LP)
1.488(0.213)
2.501(0.056)†
0.381(0.820)(LY, LM, LRCALL, LP)
1.743(0.148)
2.660(0.045)*
1.344(0.280)——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on bankloan growth are zero. The regressions include two or four lags of variables listed in
the first column, all in first differences. Dependent variables in first differences areindicated at the top of each panel. P-values are reported in parentheses. **, *, and †indicate that the restriction is rejected by the 1%, 5%, and 10% significance levels,
respectively.
＜LAG = 4＞(LY95, LP, LM)(LY95, LP, LM, RCALL)(LY95, LP, LM, LRCALL)

24

Figure 1. Money Supply Growth and Income Growth in Japan―― 1976:1-2003:4, changes from the previous year ――

A. M2 growth and real GDP growth
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
2000

2002

2002

1998

1996

1994

2000

M2 growth

1992

1990

1988

1986

1984

1982

1980

1978

1976

-0.06

Real GDP growth

B. M2 growth and nominal GDP growth

M2 growth

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06

Nominal GDP growth25

Figure 2. Bank Loans, Time Deposits+CD, and Demand Deposits in Japan―― 1975:1-2003:4 (in trillion yen) ――

600
500
400
300
200
100

Bank Loan

TD+CD

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

0

DD

Figure 3. Bank Loan Growth and Income Growth in Japan―― 1976:1-2003:4, changes from the previous year ――

0.2
0.16
0.12
0.08
0.04
0
-0.04

Bank loan growth

26

Real GDP growth

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

-0.08

Appendix
Table A-1. Predictive Content of Monetary Base——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————<LAG = 2>
A. Predicting LY95
(LY95, LP, LM)
2.258(0.110)
2.209(0.120)
0.626(0.540)(LY95, LP, LM, RCALL)
2.428(0.094)†
2.422(0.098)† 0.684(0.511)(LY95, LP, LM, LRCALL)
2.275(0.108)
2.432(0.098)† 0.719(0.494)
B. Predicting LP(LY95, LP, LM)
0.321(0.726)
0.566(0.571)
2.434(0.102)(LY95, LP, LM, RCALL)
1.223(0.299)
0.043(0.958)
1.934(0.160)(LY95, LP, LM, LRCALL)
0.078(0.925)
0.062(0.940)
2.247(0.121)
C. Predicting LY(LY, LM)
0.675(0.512)
2.136(0.127)
0.276(0.760)(LY, LP, LM)
1.159(0.318)
3.424(0.040)*
0.024(0.976)(LY, LM, RCALL)
0.398(0.673)
1.052(0.356)
0.375(0.690)(LY, LM, LRCALL)
0.657(0.521)
1.166(0.319)
0.114(0.893)(LY, LM, RCALL, LP)
0.750(0.475)
2.132(0.129)
0.037(0.964)(LY, LM, LRCALL, LP)
1.514(0.225)
2.277(0.113)
0.075(0.928)

A. Predicting LY95
2.150(0.081)†
2.454(0.059)† 0.327(0.858)
2.630(0.040)*
3.007(0.028)*
0.839(0.512)
0.778(0.542)
3.400(0.017)*
0.093(0.984)
B. Predicting LP(LY95, LP, LM)
0.091(0.985)
0.949(0.444)
0.893(0.480)(LY95, LP, LM, RCALL)
0.396(0.811)
1.091(0.373)
0.551(0.700)(LY95, LP, LM, LRCALL)
0.129(0.971)
0.824(0.517)
1.146(0.356)
C. Predicting LY(LY, LM)
1.644(0.170)
3.957(0.007)** 0.248(0.909)(LY, LP, LM)
1.923(0.113)
4.957(0.002)** 0.031(0.998)(LY, LM, RCALL)
1.301(0.276)
3.356(0.017)*
0.502(0.735)(LY, LM, LRCALL)
0.616(0.652)
3.833(0.009)** 0.609(0.659)(LY, LM, RCALL, LP)
1.552(0.194)
4.394(0.005)** 0.359(0.836)(LY, LM, LRCALL, LP)
0.777(0.543)
4.879(0.002)** 0.504(0.733)——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients onmonetary base growth are zero (LM = logged monetary base). The regressions
include two or four lags of variables listed in the first column, all in first differences.
Dependent variables in first differences are indicated at the top of each panel.
P-values are reported in parentheses. **, *, and † indicate that the restriction isrejected by the 1%, 5%, and 10% significance levels, respectively.

<LAG = 4>(LY95, LP, LM)(LY95, LP, LM, RCALL)(LY95, LP, LM, LRCALL)

27

Table A-2. Predictive Content of Stock Prices——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————<LAG = 2>
A. Predicting LY95
(LY95, LP, FIN)
4.695(0.011)*
3.022(0.057)†
2.324(0.112)(LY95, LP, FIN, RCALL)
4.858(0.010)*
3.173(0.050)†
2.243(0.121)(LY95, LP, FIN, LRCALL)
4.046(0.021)*
3.199(0.049)*
2.717(0.080)†
B. Predicting LP(LY95, LP, FIN)
0.865(0.424)
0.295(0.745)
1.724(0.192)(LY95, LP, FIN, RCALL)
0.571(0.567)
0.306(0.737)
2.937(0.066)†(LY95, LP, FIN, LRCALL)
0.841(0.434)
0.450(0.640)
1.492(0.239)
C. Predicting LY(LY, FIN)
3.869(0.024)*
3.136(0.051)†
1.354(0.270)(LY, LP, FIN)
3.949(0.022)*
3.531(0.036)*
0.743(0.483)(LY, FIN, RCALL)
4.726(0.011)*
4.081(0.022)*
1.087(0.348)(LY, FIN, LRCALL)
3.548(0.033)*
4.395(0.017)*
1.725(0.192)(LY, FIN, RCALL, LP)
4.558(0.013)*
4.164(0.021)*
0.487(0.618)(LY, FIN, LRCALL, LP)
3.606(0.031)*
4.484(0.016)*
0.926(0.406)

A. Predicting LY95
2.720(0.034)*
2.653(0.045)*
1.219(0.323)
2.622(0.040)*
2.434(0.062)†
0.984(0.433)
1.720(0.153)
2.212(0.084)†
1.026(0.412)
B. Predicting LP(LY95, LP, FIN)
0.250(0.909)
0.633(0.641)
0.747(0.567)(LY95, LP, FIN, RCALL)
0.110(0.979)
1.074(0.381)
1.364(0.273)(LY95, LP, FIN, LRCALL) 0.279(0.891)
1.403(0.249)
1.082(0.385)
C. Predicting LY(LY, FIN)
2.186(0.076)†
2.108(0.093)†
1.142(0.353)(LY, LP, FIN)
2.428(0.053)†
2.928(0.030)*
0.963(0.442)(LY, FIN, RCALL)
2.465(0.051)†
2.653(0.045)*
0.940(0.454)(LY, FIN, LRCALL)
1.470(0.218)
2.321(0.071)†
1.341(0.277)(LY, FIN, RCALL, LP)
2.513(0.047)*
3.403(0.017)*
1.211(0.329)(LY, FIN, LRCALL, LP)
1.588(0.185)
3.075(0.026)*
1.232(0.321)——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on stock
price growth are zero. The regressions include two or four lags of variables listed in
the first column, all in first differences (FIN = logged stock prices). Dependent
variables in first differences are indicated at the top of each panel. P-values arereported in parentheses. **, *, and † indicate that the restriction is rejected by the
1%, 5%, and 10% significance levels, respectively.
<LAG = 4>(LY95, LP, FIN)
(LY95, LP, FIN, RCALL)(LY95, LP, FIN, LRCALL)

28

Table A-3. Predictive Content of Exchange Rates——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————<LAG = 2>
A. Predicting LY95
(LY95, LP, FIN)
0.800(0.452)
0.700(0.501)
0.349(0.708)(LY95, LP, FIN, RCALL)
0.682(0.508)
0.620(0.542)
0.306(0.739)(LY95, LP, FIN, LRCALL)
0.728(0.485)
0.639(0.532)
0.352(0.706)
B. Predicting LP(LY95, LP, FIN)
1.388(0.254)
2.085(0.134)
0.013(0.987)(LY95, LP, FIN, RCALL)
1.283(0.282)
2.221(0.119)
0.114(0.893)(LY95, LP, FIN, LRCALL)
1.267(0.286)
1.861(0.166)
0.033(0.967)
C. Predicting LY(LY, FIN)
0.097(0.907)
0.087(0.917)
0.258(0.774)(LY, LP, FIN)
0.098(0.907)
0.007(0.993)
0.322(0.727)(LY, FIN, RCALL)
0.274(0.761)
0.027(0.973)
0.143(0.867)(LY, FIN, LRCALL)
0.099(0.905)
0.079(0.924)
0.184(0.833)(LY, FIN, RCALL, LP)
0.320(0.727)
0.070(0.933)
0.172(0.843)(LY, FIN, LRCALL, LP)
0.134(0.875)
0.146(0.865)
0.269(0.766)

A. Predicting LY95
1.116(0.354)
1.103(0.366)
0.783(0.545)
1.043(0.390)
1.100(0.369)
0.983(0.434)
1.281(0.284)
0.966(0.436)
0.725(0.583)
B. Predicting LP(LY95, LP, FIN)
2.315(0.063)†
2.210(0.082)†
0.508(0.730)(LY95, LP, FIN, RCALL)
1.045(0.389)
1.040(0.398)
0.648(0.633)(LY95, LP, FIN, LRCALL)
2.102(0.087)†
0.938(0.451)
0.551(0.700)
C. Predicting LY(LY, FIN)
0.367(0.831)
0.351(0.842)
0.978(0.432)(LY, LP, FIN)
0.264(0.900)
0.122(0.974)
1.265(0.305)(LY, FIN, RCALL)
0.515(0.725)
0.305(0.873)
0.920(0.465)(LY, FIN, LRCALL)
0.182(0.947)
0.280(0.889)
0.847(0.506)(LY, FIN, RCALL, LP)
0.474(0.755)
0.289(0.883)
1.255(0.312)(LY, FIN, LRCALL, LP)
0.138(0.968)
0.271(0.895)
1.160(0.350)——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on
foreign exchange rate growth are zero. The regressions include two or four lags ofvariables listed in the first column, all in first differences (FIN = logged yen-dollarrate). Dependent variables in first differences are indicated at the top of each panel.
P-values are reported in parentheses. **, *, and † indicate that the restriction isrejected by the 1%, 5%, and 10% significance levels, respectively.

<LAG = 4>(LY95, LP, FIN)
(LY95, LP, FIN, RCALL)(LY95, LP, FIN, LRCALL)

29

Table A-4. Predictive Content of Interest Rates——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————<LAG = 2>
A. Predicting LY95
(LY95, LP, LM, RCALL)
0.919(0.402)
1.165(0.320)
0.063(0.939)(LY95, LP, LM, LRCALL)
2.563(0.082)†
0.669(0.517)
2.887(0.069)†
B. Predicting LP(LY95, LP, LM, RCALL)
3.118(0.049)*
4.368(0.018)*
0.069(0.933)(LY95, LP, LM, LRCALL)
1.081(0.343)
4.458(0.016)*
0.290(0.750)
C. Predicting LY(LY, LM, RCALL)
1.645(0.198)
3.642(0.033)*
0.195(0.824)(LY, LM, LRCALL)
1.400(0.251)
2.463(0.095)†
1.434(0.251)(LY, LM, RCALL, LP)
1.152(0.320)
1.975(0.149)
0.088(0.916)(LY, LM, LRCALL, LP)
2.752(0.069)†
1.223(0.302)
2.500(0.097)†

A. Predicting LY95
0.617(0.652)
0.516(0.724)
1.570(0.211)
2.060(0.093)†
0.830(0.513)
2.723(0.050)†
B. Predicting LP(LY95, LP, LM, RCALL)
3.901(0.006)**
2.981(0.029)*
0.181(0.946)(LY95, LP, LM, LRCALL)
1.492(0.212)
2.516(0.055)†
1.837(0.151)
C. Predicting LY(LY, LM, RCALL)
1.045(0.388)
1.483(0.222)
0.863(0.497)(LY, LM, LRCALL)
2.088(0.089)†
1.241(0.307)
2.152(0.098)†(LY, LM, RCALL, LP)
1.009(0.407)
0.672(0.615)
1.594(0.205)(LY, LM, LRCALL, LP)
2.229(0.072)†
0.599(0.665)
3.462(0.021)*——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on
interest rate variables (RCALL or LRCALL) are zero. The money supply variable(LM) is M2. The regressions include two or four lags of variables listed in the firstcolumn, all in first differences. Dependent variables in first differences are indicatedat the top of each panel. P-values are reported in parentheses. **, *, and † indicatethat the restriction is rejected by the 1%, 5%, and 10% significance levels,
respectively.
<LAG = 4>(LY95, LP, LM, RCALL)(LY95, LP, LM, LRCALL)

30

Table A-5. Predictive Content of the Interest Rate Spread——————————————————————————————————————
Regressors
75:1-2003:4
75:1-92:4
93:1-2003:4——————————————————————————————————————<LAG = 2>
A. Predicting LY95
(LY95, LP, LM, SPRD)
0.276(0.759)
0.476(0.624)
1.958(0.156)(LY95, LP, LM, RCALL,SPRD)
0.064(0.938)
0.021(0.979)
2.175(0.130)(LY95, LP, LM, LRCALL,SPRD) 0.513(0.600)
0.092(0.913)
1.640(0.209)
B. Predicting LP(LY95, LP, LM, SPRD)
2.049(0.134)
1.634(0.205)
2.185(0.128)(LY95, LP, LM, RCALL,SPRD)
0.222(0.801)
0.486(0.618)
2.389(0.107)(LY95, LP, LM, LRCALL,SPRD) 1.408(0.250)
0.439(0.647)
2.195(0.127)
C. Predicting LY(LY, LM, SPRD)
0.898(0.411)
1.187(0.313)
5.279(0.010)*(LY, LM, SPRD, LP)
0.096(0.909)
0.098(0.907)
6.297(0.005)*(LY, LM, RCALL, SPRD)
0.336(0.716)
0.028(0.972)
5.144(0.011)*(LY, LM, LRCALL, SPRD)
1.002(0.371)
0.125(0.883)
5.496(0.008)**(LY, LM, RCALL, LP, SPRD)
0.079(0.924)
0.174(0.841)
6.367(0.005)**(LY, LM, LRCALL, LP, SPRD) 0.007(0.993)
0.063(0.939)
6.330(0.005)**

A. Predicting LY95
<LAG = 4>(LY95, LP, LM, SPRD)
0.524(0.718)
0.757(0.559)
1.699(0.179)(LY95, LP, LM, RCALL,SPRD)
0.648(0.630)
0.667(0.619)
0.570(0.687)(LY95, LP, LM, LRCALL,SPRD) 0.787(0.537)
0.862(0.495)
0.761(0.561)
B. Predicting LP(LY95, LP, LM, SPRD)
4.029(0.005)*
3.697(0.011)*
1.707(0.178)(LY95, LP, LM, RCALL,SPRD)
1.161(0.334)
0.885(0.482)
1.798(0.164)(LY95, LP, LM, LRCALL,SPRD) 3.805(0.007)*
1.422(0.245)
1.372(0.274)
C. Predicting LY(LY, LM, SPRD)
0.436(0.782)
1.878(0.130)
3.252(0.024)*(LY, LM, SPRD, LP)
0.444(0.777)
1.182(0.332)
2.845(0.043)*(LY, LM, RCALL, SPRD)
0.781(0.540)
1.508(0.217)
2.322(0.082)†(LY, LM, LRCALL, SPRD)
1.033(0.395)
2.683(0.044)*
1.775(0.163)(LY, LM, RCALL, LP, SPRD)
0.744(0.565)
1.239(0.311)
1.790(0.165)(LY, LM, LRCALL, LP, SPRD)
0.553(0.697)
1.844(0.140)
1.272(0.310)——————————————————————————————————————
Notes: This table reports F statistics testing the restriction that coefficients on theinterest rate spread (10-year government bond rate minus 3-month Gensaki rate,
denoted as SPRD) are zero. The money supply variable (LM) is M2. The regressions
include two or four lags of variables listed in the first column, all in first differences(except for SPRD). Dependent variables in first differences are indicated at the top ofeach panel. P-values are reported in parentheses. **, *, and † indicate that the restriction
is rejected by the 1%, 5%, and 10% significance levels, respectively.

31

Table A-6. Stability Test Results——————————————————————————————————————Model
CUSUM-type
Exp-LM
CHOW95 CHOW97——————————————————————————————————————
A. Predicting LY95
(LY95, LP, LM2)
0.73
11.18†
1.70†
2.07*(LY95, LP, LM2, RCALL)
0.69
14.73†
2.01*
1.74†(LY95, LP, LM2, LRCALL)
0.77
15.11*
1.66†
1.39
B. Predicting LP(LY95, LP, LM2)
0.59
20.39**
1.07
0.81(LY95, LP, LM2, RCALL)
0.53
21.33**
1.04
0.83(LY95, LP, LM2, LRCALL)
0.63
24.91**
1.10
0.77
C. Predicting LY(LY, LM2)
0.68
7.39
1.42
2.36*
(LY, LM2, LP)
0.83
14.40*
2.17*
2.60**(LY, LM2, RCALL)
0.77
9.64
1.35
1.85*
(LY, LM2, LRCALL)
0.83
9.27
1.14
1.36
(LY, LM2, LP, RCALL)
0.81
16.41*
2.16*
1.98*(LY, LM2, LP, LRCALL)
0.87
15.43*
1.82*
1.65†——————————————————————————————————————
Notes: This table reports statistics testing for parameter stability in the regression
models listed in the first column. Variables are all in first differences and dependent
variables are indicated at the top of each panel. The CUSUM-type test is proposed by
Ploberger and Kramer (1992) and rejects the stability if the model systematicallyover- or under-forecasts the dependent variable. Exp-LM is proposed by Andrews
and Ploberger (1994) and tests the null of parameter constancy against thealternative that regression coefficients shift at an unknown date. The first and last
15% of observations of the total sample are trimmed in this procedure. CHOW95 andCHOW97 are the conventional Chow tests, testing for a break in 1995:3 and 1997:4,
respectively. The regressions include four lags for all these tests. **, *, and † indicatethat the null of stability is rejected by the 1%, 5%, and 10% significance levels,
respectively.

32