Policy Duration Effects, Quantitative Monetary Easing Policy andEconomic Growth:
Growth: Evidence from Japanese Time Series Data ✝
Masafumi KOZUKA∗
Faculty of Policy Studies, University of Marketing and Distribution Sciences
3-1 Gakuen Nishi-machi, Nishi-ku, Kobe City
Hyogo, 6512188, JAPANResearch Fellow, Graduate School of Economics, Kobe University
2-1 Rokkodai-cho, Nada-ku, Kobe City
Hyogo, 6578501, JAPAN
Abstract
This paper examines the influences of policy duration effects and quantitative
monetary easing policy (QMEP) implemented by the Bank of Japan from2001-2006 on economic growth toward future periods. We employed a simple
equation with the term spread explaining economic growth, and obtained thefollowing results. The positive effects of the term spread on economic growth overthe subsequent 21 and 24 months decreased in 2001. And the estimatedcoefficients on term spread were negative and significant after the shift in bothcases. Thus, we conclude that the QMEP and policy duration effects in the 2000s
aided economic growth in Japan to some extent.

JEL classification code: E44, E52, G10

Keywords: term spread, zero interest rate policy, quantitative monetary easing
policy, policy duration effects, economic growth✝

The author wishes to thank Ryuzo Miyao (Bank of Japan) and Shigeyuki Hamori(Kobe University) for their valuable comments. The author also thanks Nobuyuki Oda(Bank of Japan) for his valuable suggestions at the 2010 Autumn Annual Meeting of
the Japan Society of Monetary Economics. Expressions of gratitude are also extended
to Okayama Shoka University and Kobe University for providing the data set utilized
in this paper. However, any remaining errors are my sole responsibility. This work
was supported by MEXT KAKENHI (19730218), Grant-in-Aid for Young Scientists(B).
∗

Corresponding author. Tel.: +81-78-796-4913
E-mail: Masafumi_Kozuka@red.umds.ac.jp

11. Introduction
This paper verifies the impacts of quantitative monetary easing policy (hereafter,
QMEP) and policy duration effects (forward guidance) on economic growth toward
future periods in Japan with a sample from 1990 to 2008. We perform empiricalanalysis of the effects of the term spread and other variables on economic growth.
The QMEP is the monetary policy regime in which the central bank sets thequantity of reserve deposit as the policy target and was first put into effect from2001-2006 by the Bank of Japan. After the Lehman shock, the Federal Reserve
Board then adopted this policy. From April 2013, the Bank of Japan also adopted
quantitative and qualitative monetary easing policies in order to eradicate
deflation. In October 2014, the FRB announced the end of the quantitative
monetary easing policy (QE3); however, several major countries and areas,
including Japan, are in a recession. Therefore, it is important to reconsider theeffects of QMEP by the Bank of Japan from 2001 to 2006.
Numerous studies have presented verification of the effects of the monetary
policy by the Bank of Japan in the QMEP era1. Okina and Shiratsuka (2004),
Oda and Ueda (2007) and other studies performed empirical analyses on term
spread. Shiratsuka and Fujiki (2001) performed measurement of the yield curvein Japan, and they show that commitment could shorten the future path ofshort-run interest rate and achieved a reduced long-term interest rate. Okina
and Shiratsuka (2004) and Oda and Ueda (2007) also show that the effect onterm spread was valid: a reduction in term spread was achieved. However,Shiratsuka and Fujiki (2001) and Okina and Shiratsuka (2004) argue that thisphenomenon could not invert the expectation of deflation; they mention that theeffects on macroeconomics are not valid. The empirical study by Kimura andSmall (2006) verifies the effects of QMEP on risk premiums and volatilities offinancial assets. This study shows that the QMEP lowered the risk premium ofcorporate bonds with high rating and the exchange rate, but increased the credit
spread of corporate bonds with low rating. It also shows that the QMEP
increased the risk premiums of securities and lowered their volatilities.

1

Detailed surveys on QMEP are shown in Ugai (2007).

2

Empirical analyses on the validity of the effect on macroeconomics variables
have also been performed. Kamata and Sugo (2006) show empirical analysis withthe sign-restriction VAR model utilizing the proxy of policy instrument that does
not face zero bound restriction. They show that the effect of monetary policy on
the price level was reduced in the zero interest rate era and QMEP era. In theempirical study by Kimura et al. (2002), the Bayesian VAR with 3 or 4-variable
model was used. They show that the effect of base money on the CPI and GDP
gap were not significant when the QMEP began. Fujiwara (2006) performs theempirical analysis utilizing the Markov Switching VAR model considering 4
variables, CPI, IIP, base money and return of long-term government bond. They
showed that the effect of the growth of money on CPI and IIP is not significant orsignificant but negligible. In the empirical study by Braun and Shioji (2006),
they analyze the effects of monetary policy on term structure with the sign
restriction VAR. They set two hypotheses on how monetary policy affects the
economy: the liquidity maintained hypothesis and costly price adjustment
hypothesis. They assume the sign restrictions following these hypotheses, andcompare the results. They show that the effects of monetary policy are transientones under the former hypothesis, but permanent under the latter hypothesis.
Honda et al. (2013) analyze the validity of the effect on macroeconomic variables
with impulse response analysis considering several monetary variables, andshow that QMEP had certain effects on output via the stock price.
In this paper, we employ a linear equation consisting of term spread(independent variable) and economic growth (dependent variable). In thisequation, the sign of the coefficient can be both positive and negative following
the mechanism described later. If it were negative, in quite another way, we cansay that policy duration effects would work, and vice versa.
Then, a brief review of the QMEP and policy duration effects is presented.
The central bank supplies a monetary base to the market and lowers the term
spread; we call this first effect here. It is expected to have a positive effect on
economic growth and price; we call this second effect here.
Economic growth is expected when the term spread is increased, and vice3

versa. This is caused by the intertemporal allocation mechanism2. However, thereduction in the term spread causes economic growth when there are two factors.
One is a high natural rate of interest. When the term spread (or long-term
interest rate) and the natural interest rate fall simultaneously, reduction in theterm spread leads to an expectation of economic stagnation. This is because a fallin the natural rate of interest reflects worsening economic conditions in the
future.
The other factor is commitment to a zero interest rate policy by the centralbank.
As Reifschneider and Williams (2000) explain, even the zero interest rate is higher
than the theoretically desirable level of the interest rate indicated by the ordinal
Taylor rule, which is mostly negative, in a recession or weak recovery. If the zerointerest rate policy is terminated with only weak recovery (i.e., nearly zeroinflation rate), the second effect would not materialize. Then, the central bank has
to announce the termination of the zero interest rate policy when robust recovery(i.e., a persistent positive inflation rate) is observed.
Here, let us contend that the commitment of the central bank to continue a
monetary easing policy (namely a zero interest rate policy) is put into effect. Then,people might forecast a short-term zero interest rate in the future with confidence
in the monetary easing policy, and the long-term interest rate would also diminish
through the mechanism of term structure. And it promotes firms’ investment andeconomic growth. These two effects—reduction in the term spread and its positiveeffects on economic growth—are called “policy duration effects.” This type of policy
is called “commitment for the duration of the zero interest rate policy.”
Figure 1 outlines these effects. This need to classify these two effects is shown inMiyao (2007), and the description in this part follows this study.
Commitment to the duration of the zero interest rate policy was put into2

It is considered that the forecasted short-term interest rate contains the

expectation of future economic conditions. For example, future economic growthis expected when the forecasted short-term interest rate increases. Then, thelong-term interest rate also increases through the mechanism of term structure.

4

effect from April 1999 to August 2000 and from February 2001 to March 2006 in
Japan. 3 In order to validate the commitments, the central bank conducts
operations on a monetary basis. Figure 2 presents plots of the growth rate of theindex of industrial production (IIP) and average balance of monetary base. Plots ofthe term spread are also presented. We utilize the non-collateral call rate as theshort-term rate and the Japanese 10-year government bond as the long-term rate
when we calculate the term spread. 4 It is shown that the monetary baseincreased after 2001 when QMEP was conducted. The spread seems to havefluctuated in response to the movement of the monetary base with a lag of about
one year. The growth rate of IIP turned positive after 2002; however, the growthrate of IIP was not high.5
Here, as described previously, we perform empirical analysis with a simple
linear equation consisting of term spread and economic growth in order to verify
the second effect, the positive influence of QMEP on production. And we also
perform the test for structural change in order to verify the shift of coefficient of
spread. The sign of coefficient turns negative if QMEP and commitment for theduration of the zero interest rate policy have certain effects.
Estimation and verification of the simple equation with the term spread andeconomic growth can make significant contributions to this field.
In Section 2, we conduct estimation and discussion. Section 3 presents theconclusion.

2. Estimation
2.1 Equation and Data
In this paper, we perform empirical analysis following the simple model
employed by Hamilton and Kim (2002) and Hamori and Bhar (2007). The model is∆y tk = α + β × spread t + u t L (1) .
3 This policy was suspended from August 2000 to March 2001. And in October 2010,
the Bank of Japan set the target rate for the collateralized call rate at 0-0.1%.
4 Data on the average balance of base money are obtained from the web page of the
Bank of Japan. The data source of the interest rate and IIP is IFS (International
Financial Statistics) published by the International Monetary Fund in 2008 (CD-ROM
version).
5 In Oda and Ueda (2007), it is mentioned that the money multiplier decreased at that
time.

5

Here, ∆y tk is economic growth from period t toward t+k where k=6, 9, 12, 15,
18, 21, and 24. And spreadt is the term spread, α is a constant, β is thecoefficient on spread, and ut is disturbance. β is the most important parameterin this study, representing the effects of the spread on economic growth.
We utilize the monthly data from January 1990 to December 2008: thenon-collateral call rate as the short-term rate ( crt ), the Japanese 10-year
government bond as the long-term rate ( lrt ), and the index of industrial
production (IIP) as production67 . However, we employ data on economic growthover 24 months in advance at maximum, and can utilize data from January 1990
to December 2006. The definitions of the variables are

spreadt = lrt − crt L(3)and∆ytk =

1200 yt + kln
L(4) ,kyt

where k=6, 9, 12, 15, 18, 21, and 24.

2.2 Estimation and Test of Structural Change
We now estimate equation (1) using the generalized method of moments(GMM) in order to avoid the endogeneity problem. We set five instrumental
variables for the model:

spread t −1 , spread t −2 , ∆yt3−4 , ∆yt3−5 , const . .
Here, const . denotes constant term. These settings are the same as those of3

the augmented terms in Hamilton and Kim (2002). For example, ∆yt − 4 denotes
the annual rate of quarterly economic growth at one month before. We performed

6

The data source of the spread and IIP is the same as Figure 3: IIP is IFS(International
Financial Statistics) published by the International Monetary Fund in 2008 (CD-ROM
version).
7 We can utilize only monthly or quarterly data of IIP, though higher frequent data of
the financial market can be obtained. Therefore, we perform empirical analysis withmonthly data in order to ensure a sufficient sample size.

6the unit root test of

spread t and ∆y t1 with the ERS test (with constant andtrend) developed by Elliott, Rothenberg and Stock (1996). Each test statistic is-2.874 and -3.167. Each test statistic indicates rejection of null of unit root8. The
critical value of the 10% significant level is -2.365 and that of 5% is -2.927. They
are not robust; however, we assumed the instruments of each industry arestrongly stationary here.
The results of GMM estimation for equations (1) are shown in Table 1. These
results show that the effects of the term spread (the size of β̂ ) lessen as we
consider economic growth toward a longer horizon.
However, the P-value of J-statistics—test statistics for the over-identifying
restriction—is between 0.05 and 0.25 except for the case with k=15. Considering
the problem of power, these results may not be robust. In addition, the shift ofparameter β is an important object of this study. Therefore, it is necessary to
perform a test for structural change.
Here, in order to detect one unknown structural change point with GMM
estimation, we employ the sup-predictive test developed by Ghysels et al. (1998).
This test has its basis in the predictive test derived by Ghysels and Hall (1990).
The results of the sup-predictive test show that there is a structural change withthe model explained by equation (1) when we employ economic growth toward 21
months and 24 months in advance. The detected point of structural change is
October 2001 (k=21) and September 2001 (k=24). The details are shown in Table2.

2.3 Sign of the Coefficient after
after a Shift
Does the term spread truly have a negative effect on economic growth after ashift? In order to verify this point, we estimated equation (1) in the second
subsample. The instrumental variables of the GMM estimation are the same as
those employed in the previous estimation. The results of the estimation in thesecond subsample are shown in Table 3. These results show that the effects of the8

We can rewrite∆y tk as (1 k ) × (∆ y t1+ k −1 + L + ∆ y t1 ) . And thus, when ∆yt1 iskstationary, we can say that ∆ y t

is also stationary.

7term spread are negative. With this model, the estimated coefficients of term
spread are negative and significant in both cases, when k = 21 and 24. And in bothcases, the J-statistics indicate that over-identifying restrictions are formed; theP-values are at least 0.278.

3. Conclusion
The empirical analyses performed in this paper reveal certain findings.
Firstly, the model shown by equation (1) has a structural change in September(k=24) and October 2001 when k=21. And the results of estimation with thesample after the break show that the effects of the term spread are negative inboth cases.
In March 2001, the quantitative monetary easing policy (QMEP) and policy
duration were performed with the commitment of the Bank of Japan. The
structural changes in the relation between the term spread and economic growthwere, probably, caused by these monetary policies newly put into effect. It wasalso shown that the coefficients on the term spread with the simple model withequation (1) are negative and significant when we employ the second subsample,
which begins in October 2001 (with economic growth toward the 21 months inadvance) or September 2001 (with the 24 months in advance).
As described previously, there are two aspects to the policy duration effects:
The first is the reduction in the term spread caused by a commitment to monetaryeasing policy and by an expectation of a continuous low (zero) interest rate, andthe second is that the expectation of monetary easing and reduction in the term
spread are effective in promoting economic growth. Following these results of theempirical analyses, we conclude that QMEP and policy duration effects (from2001 to 2006) have some influence on the second aspect in the Japanese economy.
However, the empirical analysis performed in this paper is based on a simple
equation consisting of economic growth, term spread and other financial variables.
In order to verify the policy duration effects in Japan in detail, we have toconsider other macroeconomic variables: stock price, exchange rate, andgovernment expenditure. To verify the effects of these variables, approaches basedon the VAR model are useful, as shown by Honda et al. (2013) and other studies.
Several approaches applying the VAR model have been developed in many studies.

8

As future study, we will estimate utilizing factor-augmented VAR (FAVAR) or
expectation-augmented VAR considering the other important variables9.

References

Bhar, Ramaprasad and Shigeyuki Hamori. 2007. “Analysing Yield Spreadand Output Dynamics in an Endogenous Markov Switching Regression
Framework.” Asia-Pacific Financial Markets, 14, pp. 141-156.



Braun, R. Anton and Etsuro Shioji. 2006. “Monetary Policy and the Term
Structure of Interest Rates in Japan.” Journal of Money, Credit and Banking,38(1), pp.141-162.

Elliott, G., T. J. Rothenberg and J. Stock (1996), “Efficient Tests for an
Autoregressive Unit Root.” Econometrica, 64(4), pp.813-836.

Fujiki, Hiroshi and Shigenori Shiratsuka. 2001. “Policy Duration Effectunder the Zero Interest Rate Policy in 1999-2000: Evidence from Japan’s
Money Market Data.” Monetary and Economic Studies, Vol.20, No.1, Bank ofJapan's Institute for Monetary and Economic Studies (IMES).



Fujiwara, Ippei. 2006. “Evaluating Monetary Policy When Nominal Interest
Rates are Almost Zero.” Journal of the Japanese and International Economies,
Vol. 20(3), pp.434-453.

Ghysels, Eric and Alastair Hall. 1990. “A Test for Structural Stability of Euler
Conditions Parameters via the Generalized Methods of Moments Estimator.”

International Economic Review, 31(2), pp. 355-364.


Ghysels, Eric, Alain Guay, and Alastair Hall. 1998. “Predictive Test for
Structural Change with Unknown Breakpoint.” Journal of Econometrics,
82(2), pp. 209-233.

Hamilton, J.D. and D. H. Kim. 2002. “A Re-examination of the Predictabilityof Economic Activity Using the Yield Spread.” Journal of Money, Credit andBanking, 34(2), pp.340-360.


Honda, Yuzo, Yoshihiro Kuroki, and Minoru Tachibana. 2013. “An Injection ofBase Money at Zero Interest Rates: Empirical Evidence from the JapaneseExperience 2001-2006.” Japanese Journal of Monetary and Financial

9 For example, Shibamoto (2007) performs empirical analysis on the Bank of Japan’s
monetary policy with FAVAR approach.

9Economics, 1(1), pp. 1-24.


Kamada, Koichiro and Tomohiro Sugo. 2006. “Evaluating Japanese Monetary
Policy under the Non-negativity Constraint on Nominal Short-term Interest
Rates." Bank of Japan Working Paper Series.” No.06-E-17 (October 2006),
Bank of Japan.



Kimura, Takeshi, Hiroshi Kobayashi, Jun Muranaga, Hiroshi Ugai. 2002.
“The Effect of the Increase in Monetary Base on Japan's Economy at Zero
Interest Rates: An Empirical Analysis.” IMES Discussion Paper No. 2002-E-22,
Bank of Japan's Institute for Monetary and Economic Studies (IMES).



Miyao, Ryuzo. 2007. “Quantitative Easing Policy and Policy Duration Effects”(Ryoteki-Kanwa Seisaku to Jikanjiku Kouka). Journal of Economics andBusiness Administration (Kokumin-Keizai Zasshi) 195(2), pp. 79-94 (inJapanese).


Oda, Nobuyuki and Kazuo Ueda. 2007. “The Effects of the Bank of Japan's
Zero Interest Rate Commitment and Quantitative Easing on the Yield Curve:
A Macro-Finance Approach.” Japanese Economic Review 58(3), pp.303-28.

Okina, Kunio and Shigenori Shiratsuka. 2004. “Policy Commitment andExpectation Formation: Japan's Experience under Zero Interest Rate.” North

American Journal of Economics and Finance 15(1), pp. 75-100.
Reifshneider, David and John C. Williams. 2000.

“Three Lessons for

Monetary Policy in a Low-Inflation Era.” Journal of Money, Credit andBanking, 32(4), November, part 2: Monetary Policy in a Low-Inflation
Environment, pp. 936-966.
Shibamoto, Masahiko. 2007. “An Analysis of Monetary Policy Shocks inJapan: A Factor Augmented Vector Autoregressive Approach.” Japanese

Economic Review, 58(4), pp.484–503.
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Empirical Analyses.” Monetary and Economic Studies, Vol.25, No.1, Bank ofJapan's Institute for Monetary and Economic Studies (IMES).

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Figure 1

Policy duration effects

Expected lower short-term
interest rate

Lower long-term rateCommitment of zero-interest rate untilrobust recovery is observed<1st effect >Reduction of term spreadHigh natural rate of interestContinuation of zero-interest

Low natural rate of interest

rate until robust recovery isobserved<2nd effects>

Economic growth

Economic stagnation

Coefficient of spread:

Coefficient of spread:negative

positive(Note) Based on Oda and Ueda (2005) and Miyao (2007), the author devises thisfigure. The first effect implies the reduction in term spread. The second effect isthat on the economic growth. With the high natural rate of interest andcommitment to zero interest rate policy by the time robust recovery is observed,
the second effect would materialize. These two effects are called “policy duration
effects.”

11

FigureFigure 2 Spread, IIP,
IIP, and monetary base (unit: %)Source: Data on the average balance of base money are obtained from the web page of
the Bank of Japan. The data source of the interest rate and IIP is International
Financial Statistics (IFS) published by the International Monetary Fund.

12

Table 1. GMM estimation

k =6
k =9
k =12
k =15
k =18
k =21
k =24

J -stat.[P- value]β (Standard Error)
2.451(0.700)***2.481(0.685)***2.406(0.608)***2.331(0.513)***
1.996(0.494)***1.860(0.471)***1.653(0.475)***6.813[0.078]
6.813[0.078]
7.079[0.069]
5.596[0.133]
4.066[0.254]
5.492[0.139]
5.815[0.121]
5.744[0.125]

Note: *** denotes a 1% level of significance. J-statistics obey

χ 2 distribution with

DF=3.

Table 2. SupSup-predictive test

Sup-predictive Test Statistics
14.1199.825
13.55
13.49418.93
25.321***34.883***

k =6
k =9
k =12
k =15
k =18
k =21
k =24

Detected Breakpoint
Nov. 1993
Sep. 2001
Sep. 2001
Sep. 2001
Sep. 2001
Oct. 2001
Sep. 2001

Note: Critical values of sup-predictive test are as follows: 5% (19.20), 1% (23.56) and*** denotes a 1% level of significance.

Table 3. SubSub-sample estimation(k=21,Oct. 2001-Dec. 2006)
β (Standard Error) J -stat.[P -value]-1.044(0.465)**
3.849[0.278](k=24,Sep. 2001-Dec. 2006)
β (Standard Error) J -stat.[P -value]-1.011(0.433)*** 2.429[0.658]
Note: J-statistics obey

χ 2 distribution with DF=3 (in base model). ** denotes a 5%

level of significance and *** a 1% level of significance.

13