DP2020-15

Japan's Monetary Policy:
A Literature Review and
Empirical Assessment
Masahiko SHIBAMOTO
Wataru TAKAHASHI
Takashi KAMIHIGASHI

Revised March 16, 2021

Japan’s Monetary Policy: A Literature Review and Empirical
Assessment*
Masahiko Shibamoto, Wataru Takahashi, Takashi Kamihigashi§
March 6, 2021

Abstract
This study reviews and assesses the monetary policy implemented by the Bank of Japan(BOJ), focusing on policies that employ short-term interest rates as the operational target.
Our review of empirical studies on monetary policy that inﬂuenced the BOJ’s policy reveals
that they focused on (1) banking system and interest rate mechanisms, (2) ﬁnancial deregulation and monetary aggregates, (3) the systematic reaction regarding the achievement of
the ultimate goal, and (4) conﬁrming how certain evidence serves as material for policy
judgments and communication with the public. Our empirical results on the causal eﬀect ofmonetary policy in the framework of a structural vector autoregressive model attest to thesigniﬁcant impact of Japan’s monetary policy on the ﬁnancial market and macroeconomy
from the 1980s onward. Our counterfactual simulations aﬃrm that the central bank should
consistently shift its policy stance to achieve macroeconomic stability. Even small policy ratecuts in a low-interest-rate environment make signiﬁcant contributions to economic recovery.
Keywords: Japanese macroeconomy, Short-term interest rate, Causal eﬀect of monetary
policy, Counterfactual simulation, Vector autoregressive model.
*

We are grateful to Masato Shizume and two anonymous referees for helpful comments and discussions. We
would also like to thank Miho Kohsaka and Mayura Takami for providing excellent research assistance. This
study received ﬁnancial support from the Japan Society for the Promotion of Science (JSPS KAKENHI, Grant
Numbers 15H05729, 17K03804, and 20H05633).
Corresponding author: Masahiko Shibamoto, Research Institute for Economics and Business Administration(RIEB) and Center for Computational Social Science (CCSS), Kobe University, 2–1, Rokkodai, Nada, Kobe,
657–8501, Japan. E–mail: shibamoto@rieb.kobe-u.ac.jp.
Wataru Takahashi, Faculty of Economics, Osaka University of Economics, 2–2–8, Osumi, Higashiyodogawaku, Osaka, 533–8533, Japan. E–mail: wtaka@osaka-ue.ac.jp§
Takashi Kamihigashi, Research Institute for Economics and Business Administration (RIEB) and Center for
Computational Social Science (CCSS), Kobe University, 2–1, Rokkodai, Nada, Kobe, 657–8501, Japan. E–mail:tkamihig@rieb.kobe-u.ac.jp.

1

Introduction

The empirical assessment of monetary policy transmission is a long-standing issue for contemporary policymakers and researchers in the ﬁeld of quantitative monetary economics. Whilevarious studies examine how monetary policy aﬀects the economy using real-world data, aconsensus has yet to be reached. The economic impacts of a policy are challenging to assessthrough empirical means in any economy, including that of Japan. The diﬃculty of empiricalpolicy assessment in Japan can be easily conﬁrmed by the data, especially in recent years.
Monetary policy contributes to the rapid economic development of underdeveloped countries
by supplying ample credit. Figure 1 shows that Japan experienced high economic growth andinﬂation in the three decades of economic development following the 1950s, with lulls during theoil shocks of 1973 and 1979. Japan still enjoyed good macroeconomic performance throughout
the 1980s to the beginning of 1990; real GDP grew by approximately 5 percent per year, andinﬂation was mild. Japan’s success in catching up to the advanced economies was supported by
the provision of suﬃcient bank credit and money supply even when short-term interest rates
were high. Table 1 shows that the rate of real GDP growth was strongly associated with thegrowth of money stock and bank credit during that period. The monetary policy of the Bankof Japan (BOJ) prior to and during the 1980s was successful in supplying credit for economic
development.1
Table 1: Comparison of split-sample correlation coeﬃcients for four-quarter growth of realmoney stock, bank credit, and GDP.
Sample period
Correlation between
Money stock and Bank credit
Money stock and GDP
Bank credit and GDP

1975Q1–1984Q4

1985Q1–1993Q4

1994Q1–2019Q1

0.96[0.00]
0.73[0.00]
0.68[0.00]

0.94[0.00]
0.81[0.00]
0.83[0.00]

0.09[0.35]
0.13[0.20]
0.20[0.04]

Notes: This table reports the Pearson’s correlation coeﬃcients between two variables. The numbers in brackets
are p-values for testing the null hypothesis that two variables are uncorrelated.

After becoming a major developed country in 1985, Japan experienced overheated economic
1
As Shizume (2018) describes, the ultimate goal of monetary policy in Japan from the late 19th century
through the 1970s was to provide credit for development.

1

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Figure 1: Financial market and macroeconomy in postwar Japan
Notes: The sample period depends on the data availability. The oﬃcial discount rate is from January 1955 throughMarch 2019. The growth of the monetary base is from January 1971 through March 2019. The collateralized
and uncollateralized overnight funding rate (call market rate) is from January 1955 through April 2016 and July
1985 through March 2019, respectively. The growth of the money stock is from January 1968 through March
2019. The stock prices are from January 1955 through March 2019. The growth of bank credit is from the
1st quarter of 1964 through the 1st quarter of 2019. The GDP deﬂator inﬂation rate and the growth of thereal GDP calculated based on the system of national accounts compiled according to 1968 SNA, 1993 SNA, and2008 SNA are from the 1st quarter of 1956 through the 1st quarter of 2001 and the 1st quarter of 1981 throughthe 1st quarter of 2019, respectively. The dark gray lines in the middle left panel indicate the time series of
the collateralized overnight call market rate and the black lines indicate the time series of the uncollateralized
overnight call market rate. The dark gray lines in the panels on the lower left and right indicate the values basedon the 1968 SNA and the black lines indicate the values based on the 1993 SNA and 2008 SNA. The shaded
areas show periods of recession in Japan, as deﬁned by the Cabinet Oﬃce.

activity, from an asset price bubble to a long-lasting economic stagnation with sluggish monetaryconditions. The Japanese bubble expanded rapidly in the second half of the 1980s, culminatingin the peaking of the Nikkei 225 Stock Index at almost 40,000 yen by the end of 1989. Then,the bubble burst, with a sudden collapse of stock prices at the beginning of 1990. The event wasa hard hit to the monetary and macroeconomic conditions of Japan: growth rates of the realmoney stock, bank credit, and GDP sharply rose and fell together over the 1985–1993 period.
Japan suﬀered through a long period of economic stagnation after the end of the recession causedby the bubble burst. The growth rates of real bank credit and GDP both average less than 1
percent, which was far below their levels before 1993. The relationship between monetary and2macroeconomic conditions also seems to have vanished during the period of economic stagnation.
Table 1 shows that correlation coeﬃcients for four-quarter growth of real money stock, bankcredit, and GDP after 1993 are only 0.09, 0.13, and 0.20, respectively, much less than what they
were before 1993.
Moreover, the coexistence of economic boom/stagnation and a sequence of short-term interest rate hikes and cuts (see Figure 1) seems to raise concerns about the eﬀectiveness of monetary
policy.2 Table 2 presents simple linear regressions of real money stock, bank credit, and GDP
on the overnight funding rate using three subsamples: 1970 to 1984, 1985 to 2001, and 2001 to
the end of the sample. The relationship between money stock, bank credit, real output, and theshort-term interest rate before 1985 is consistent with what economic theory predict typically:
the decrease in the short-term interest rate, which implies expansionary monetary policy, isassociated with increases in the future money supply, bank credit, and real output. This relationship, however, has been unstable since 1985: the estimated coeﬃcients of the short-term
interest rate on the future money stock, bank credit, and real output are either contrary to thetheory or statistically insigniﬁcant.
Table 2 does not necessarily demonstrate that monetary policy is no longer eﬀective. The
systematic policy response to economic conditions dramatically complicates eﬀorts to discern
the causal eﬀects of policy on the economy. In practice, many central banks systematicallydecide their policy stance to maintain macroeconomic stability. Speciﬁcally, the BOJ lowered
its short-term interest rate in response to the economic downturn caused by the bubble burst.
If the monetary policy was eﬀective, it could be expected to mitigate the fall in GDP growth.
We also ﬁnd that the short-term interest rate had either a positive association with real GDP
growth or no association. To assess the eﬀectiveness of the monetary policy, we specify thecausal relationship between the policy actions and outcomes by thoroughly disentangling thesystematic responses of the types and the other components (i.e., the so-called “shocks” ofmonetary policy). The empirical literature strives to advance the methodology for measuring
the eﬀect of a monetary policy “shock” in the presence of systematic responses to the economy.
2

Empirical studies such as Miyao (2000), Fujiwara (2006), Inoue & Okimoto (2008), and Nakajima et al.
(2011) note a structural change in the role of Japanese monetary policy during the 1990s and a loss of policy
power due to a breakdown in the channel of transmission between short-term interest rate adjustments and thereal economy. However, Honda & Kuroki (2006) and Shibamoto (2016) argue that we would underestimate
the policy impacts if the empirical models failed to control for the market expectation of the monetary policy
stance. Moreover, they ﬁnd that Japan’s monetary policy still inﬂuences the ﬁnancial market and real economy
signiﬁcantly even after the 1990s.

3

Table 2: Comparison of coeﬃcient estimates from split-sample linear regression models, regressing four quarters ahead of the real money stock, bank credit, and GDP on the overnight funding
rate.
Sample period
Dependent: 4 quarters ahead of
Money stock
Bank credit
GDP

1970Q1–1984Q4

1985Q1–2001Q1

2001Q2–2019Q1-10.62(4.74)-10.55(4.69)-5.82(2.01)-3.48(2.18)
2.69(2.74)
2.44(2.93)-12.49(12.93)
19.94(21.41)-20.37(10.79)Notes: The dependent variable is four quarters ahead of the log of real money stock, real bank credit, and real
GDP (1968SNA for sample 1970Q1-1984Q4 and 1993SNA/2008SNA for 1985Q1-2001Q1 and 2001Q2-2019Q1)
times 400. The independent variable is the overnight funding rate (collateralized overnight call rate for 1970Q11984Q4 and 1985Q1-2001Q1, uncollateralized overnight call rate for 2001Q2-2019Q1). The constant and oneto four-quarter lags of each variable of interest and overnight funding rate are included as control variablesin the linear regression model. The numbers in parentheses are Newey & West (1987) heteroskedasticity andautocorrelation robust standard errors for least squares with a four-quarter lag truncation. Coeﬃcients andstandard errors for the control variables are not reported.

This study aims to contribute to the empirical literature on Japan’s monetary policy in two
diﬀerent ways. First, we introduce the historical background of empirical studies on Japan’s
monetary policy to researchers in broad-ranging ﬁelds, including computational social science.
Next, we assess the recent development of monetary policy using the standard empirical methodology in the literature to stimulate further discussion on the role of monetary policy in developed
countries that face policy response challenges in the aftermath of extraordinary shocks such as
the bursting of the Japanese bubble economy or the 2008 global ﬁnancial crisis in a low-interestrate environment.
The rest of this paper is organized as follows. Section 2 reviews the empirical studies on
Japan’s monetary policy retrospectively and considers the historical and economic backgroundof the study. Section 3 presents an overview of Japan’s macroeconomy and monetary policy
since the 1980s. Section 4 describes the empirical model employed to make causal inferences on
the eﬀect of monetary policy and reports the empirical results. Section 5 conducts counterfactual simulations based on our empirical model to provide lessons for monetary policy strategy
according to Japan’s experience since the 1980s. Section 6 concludes. The Appendix provides
detailed deﬁnitions of the variables and data sources.

4

2

A historical review of empirical studies that aﬀected the BOJ’smonetary policy

This section considers the empirical studies on Japan’s monetary policy retrospectively to clarify
how the studies examined the eﬀectiveness of the monetary policy. It primarily considers studies
by researchers who worked for or with the BOJ. While a wider range of research was conductedin academia, we consider studies that seem to have a relatively signiﬁcant inﬂuence on the BOJ’s
policies. Our survey also considers the historical and economic background of the studies. 3
Active empirical research on monetary policy has been conducted in Japan since the late1960s. Intriguingly, the focus of early research was on aspects of banking behavior, such as
how the BOJ’s policies aﬀected bank lending and interest rate mechanisms. By the 1980s, themainstream focus of empirical research on monetary policy centered on the role of the money
supply. While the methodology changed with techniques such as regression analysis and timeseries analysis, the analysis consistently focused on three variables: monetary aggregates, realGDP, and prices. Bank behavior ceased to be the explicit topic of interest in the research. On
the other hand, a pioneering study was conducted on the goal of monetary policy and its policy
reaction function. Currently, the BOJ has utilized empirical analyses to conﬁrm evidence thatserves as material for actual policy judgments and communication with the public.

2.1

Empirical analysis of window guidance: Special features of Japan’s ﬁnancial structure and eﬀectiveness of monetary policy before the 1980s

Patrick (1962) had a signiﬁcant impact in the early stages of analyzing Japan’s monetary policy.
Regarding Japan’s ﬁnancial structure, he noted that (1) banks heavily borrowed from the BOJ,
(2) businesses relied on borrowing from banks, and (3) interest rates were rigid. 4 Thus, heconcluded that monetary policy was eﬀective by (1) and (2) but not necessarily by (3). Royama(1971) highlighted that simply applying a Keynesian macroeconomic model was misleading insituations where indirect ﬁnance was overwhelming, and interest rates were rigid.
3

See also Ito & Hoshi (2020) on the history of the Japanese ﬁnancial market and monetary policies.
Japanese commercial banks consist of a small number of large city banks based in urban areas and a large
number of relatively medium-sized regional banks based in rural areas. Until the 1970s, industrialization in Japan
took place predominantly in urban areas and funds ﬂowed from rural areas into urban areas which supported
economic development in Japan. As Japan was a bank-dominant ﬁnancial system, funds also ﬂowed from regionalbanks into city banks. The city banks then ﬁnanced large corporations as the main bank. For more detailed
discussion on the institutional background of the banking sector in Japan, see, for example, Takahashi (2012).
4

5

The empirical analysis at that time revolved around the eﬀectiveness of the BOJ’s window
guidance, a policy instrument that directly regulated bank loan volume. 5 Royama (1971) also
pointed out that the Japanese business cycle relied on corporate capital investment, which inturn relied exclusively on bank loans. Hence, the appropriate approach for analyzing monetary
policy eﬀects was thought to be the bank sector model rather than the conventional macroeconomic model. Research on Japan’s monetary policy emphasized direct control channels by the
BOJ while de-emphasizing the interest rate mechanism (Komiya, 1964; Tachi, 1965).
However, the empirical analysis of the BOJ economist Suzuki (1966) emphasized the role ofthe interest rate mechanism by focusing on the interbank market (call market), where interest
rates moved relatively freely. Suzuki’s work made an important contribution by empiricallydemonstrating the eﬀectiveness of monetary policy through the interest rate mechanism ofinterbank rate ﬂuctuations, using data from 1954 to 1964. If window guidance had been the mosteﬀective monetary policy instrument, city bank lending, an activity subject to window guidance,
would have ﬂuctuated more widely. Suzuki found, however, that the lending volume of regionalbanks moved more ﬂexibly than that of city banks because the interbank rate was ﬂexible. He
argued that the monetary policy at that time was aﬀected to some extent by the interest ratemechanism. However, his ﬁndings only partially showed that the interest rate mechanism wasworking and did not refute the eﬀectiveness of window guidance. These observations led Suzuki
and other BOJ economists to assert that window guidance was complementary to interest rate
policy.
In fact, subsequent empirical studies gave a negative evaluation of the eﬀectiveness of Japan’s
window guidance as an independent policy instrument. Horiuchi (1977, 1980) emphasized theimportance of a general equilibrium, rather than a partial equilibrium, perspective and reported
that the eﬀectiveness of window guidance was not signiﬁcant.6 Horiuchi’s results sparked controversy over the eﬀectiveness of window guidance because denying the eﬀectiveness of window
guidance denied the BOJ’s policies at the time in the sense that the window guidance played
5

Window guidance was a complementary measure for the BOJ’s interest policy from the 1960s to the 1970s.
Even within voluntary practices, city banks submitted lending plans every quarter to be approved by the BOJ, andthe results were carefully monitored. However, since the 1980s, the demand for funds from large corporations has
decreased due to a decline in the economic growth rate. This has caused the situation to change. See Appendix
4 in Itoh et al. (2015) on the development of window guidance in Japan.
6
Horiuchi (1977, 1980) constructed a general equilibrium model where the monetary economy was divided
into sectors (BOJ, government, private banks, and non-private banks) and assets (cash, government bonds, BOJ
loans, interbank loans, deposits, securities, and bank loans). His empirical analysis covered the eﬀectiveness of
window guidance from 1963 to 1974. Bank lending volume was used as the dependent variable, and the interbank
rate and window guidance were used as the explanatory variables.

6

a critical role in controlling the market interest rate. Another BOJ economist, Eguchi (1977),
argued that window guidance, along with other policies, curbed lending. A dispute on thispoint, the so-called “Horiuchi-Eguchi Controversy,” arose between academics and the BOJ. An
empirical study by Furukawa (1981) on the eﬀect of the interbank rate on bank reserves empirically showed that bank reserves were sensitive to the interbank rate, and window guidance
was eﬀective as a complementary tool to support interest rate policy.
The argument that the BOJ sought to stabilize the market interest rate by using discountwindow lending, as well as the open-market operation, was supported by the view that the
BOJ rationed discount window lending among private banks and regulated the quantity ofborrowing.7 Credit rationing by window guidance came under renewed focus in an empirical
disequilibrium analysis developed from Keynesian disequilibrium economics. In such an analysis
on credit rationing in the Japanese bank loan market, Iwata & Hamada (1980) found evidencein support of the credit rationing view on the BOJ’s discount-window policy. 8

2.2

Empirical studies that inﬂuenced monetary policy in the 1980s: Emphasis on monetary aggregates and policy analysis with time-series model

The move towards empirical analysis progressed signiﬁcantly in the 1980s. Several importantchanges took place in the course of ﬁnancial liberalization and deregulation. (1) Interest rates
were progressively liberalized. (2) The supply of much-needed funds in the corporate sector
brought induced major changes in the money ﬂow. (3) Corporate ﬁnancing expanded beyondbank loans as the capital markets developed. These changes increased the eﬀectiveness of
the analysis in conventional macroeconomic models, and empirical analysis replaced standard
macroeconomic analysis.
The most important study on monetary policy in the 1980s, given its inﬂuence, was conducted by the Bank of Japan (1975). With a sample spanning 1957 to 1974, divided into threesub-sample periods, the BOJ study showed that changes in the money stock (M2+CD) preceded
7
Shinohara & Fukuda (1982) argued that the guidance measure could be a complementary tool to ensure astable supply of money and bank credit because the BOJ, which sought to stabilize the market interest rates, did
not practice rigid monetary base control in situations with an unstable demand for bank reserves in the shortrun.
8
In the measurement period from 1960 to 1973, Iwata & Hamada (1980) found a disequilibrium in the Japaneseloan market and determined that lending rates were also aﬀected by regulatory interest rates. Furukawa (1979),
however, employed data from 1960 to 1977 to show the result of denying disequilibrium. Comparing the U.S. andJapanese lending markets, Ito & Ueda (1981) found disequilibrium in both markets and determined that interest
rate adjustments were slower in Japan than in the U.S.

7

GDP and price changes in each period. This result provided the basis for the money supply
target policy, which emphasized money stock as an intermediate target in monetary policy. In
fact, the BOJ has also targeted the money stock as an implicit target since 1978. For a policy
targeting money stock to be eﬀective, the demand for money must be stable. As a supplement,
the Bank of Japan (1975) measured the money demand function over the 1964–1974 period. 9
The BOJ’s ﬁnding that money preceded GDP and prices was based on simple time-lagged
correlations. Using a methodology proposed by Sims (1972) to test a concept of causality basedon prediction, Oritani (1979) reported that changes in the money stock preceded changes innominal GDP over the 1962–1976 period.10 Moreover, Oritani (1981) used a three-variable
time-series model of money stock, real GDP, and prices to show that changes in the moneystock preceded changes in real GDP and prices over the 1956–1979 period.11 Oritani’s timeseries analysis thereafter leads to the vector autoregressive (VAR) analysis.

2.3

Empirical analysis of systematic monetary policy: Changes in the policy
reaction function

To the best of our knowledge, the ﬁrst empirical investigation of the BOJ’s reaction function
was a study by Kaizuka (1967) using data from the 1956–1963 sample period. The ﬁnal goalof the monetary policy at the time could have been outcomes such as inﬂation, the economic
growth rate, and the balance of payments (Dewald & Johnson, 1963). In addition to the moneystock, Kaizuka (1967) also used the interbank rate, bank loan volume, and other factors. His
results indicated that the BOJ conducted monetary policy by controlling the interbank rate
and adjusted the interbank rate, considering the balance of payments. Moreover, its policy
goal at the time was to balance the balance of payments. This result was consistent with theﬁndings from the aforementioned study by Suzuki (1966), and the goal of balancing the balanceof payments was consistent with the rules of the game under ﬁxed exchange rates.
Subsequent empirical studies noted that the BOJ’s reaction function changed in the 1970s
9
To the best of our knowledge, the ﬁrst empirical study on Japan’s money demand function was Ichimura(1962). Eguchi & Sawa (1979) measured the money demand function respectively by the household and corporate
sectors, considering special features of the Japanese ﬁnancial structure such as the indirect ﬁnancial advantage
and interest rate regulation.
10
Oritani (1979) also used the total expenditure function of the St. Louis Fed to report the precedence of themoney stock to nominal GDP.
11
Miyao (2005) employs an econometric methodology similar to that of Oritani (1981) with data extending
to the end of 2003 to re-examine whether the money stock (M2+CDs) can predict future economic activity andprices. He ﬁnds that the linkage between M2 and income or prices has been unstable since the 1990s.

8as the focus of its policy goal began to shift toward macroeconomic stability. When Seo &Takahashi (1982) divided the measurement period into the 1960s and 1970s, for example, theyfound that the bank initially had a reaction function that emphasized the external balance of
payments in the 1960s. The bank then emphasized domestic factors such as the GDP and prices
in the 1970s.12
Upon ﬁnding that the economic conditions well explained the shifts in policy stance, researchers were intrigued by the possibility that systematic and predictable monetary policy
aﬀected the economy in an important way.13 In response to the Lucas-Sargent-Wallace (LSW)
hypothesis that an anticipated change in monetary policy would not aﬀect real output, 14 Seo &Takahashi (1982) reported a statistically signiﬁcant eﬀect of anticipated changes in monetary
policy.15 For further discussion on the LSW hypothesis, see, for instance, Hamada & Hayashi(1985), in addition to Seo & Takahashi (1982).

2.4

Empirical analysis at battleﬁeld since the 1990s: Materials for policy
judgment and communication

The BOJ has used market functions in implementing monetary policies under the progress ofﬁnancial liberalization and deregulation since the 1980s. In practice, their progress gradually
undermined the need for window guidance in Japan. Given liberalization, the BOJ can adjust
the overnight funding rate freely determined in the market simply through open-market operations. The BOJ ultimately terminated window guidance in 1991. Following this, the BOJ
oﬃcially changed monetary policy measures from those based on regulated interest rates, i.e.,the oﬃcial discount rates, to those based on market operation with the use of money markets
and ﬂexible interest rates. It began to guide the short-term market interest rates as an operating target for market operations in 1995, and decided to use the uncollateralized overnight callrate as a speciﬁc target for market operations in 1998.16
12

Clarida et al. (1998), Bernanke & Gertler (1999), Jinushi et al. (2000), and Shibamoto (2008) describe the
BOJ’s behavior from the 1970s to the early 2000s by estimating the reaction function where the short-term policy
rate depends on the output gap and inﬂation.
13
Bernanke et al. (1997) and Sims & Zha (2006) propose a methodology to explore the eﬀects of systematic
monetary policy on the economy within a structural VAR framework.
14
The term “LSW hypothesis” has been in common use since Gordon (1982).
15
Regarding their proof, a problem of multicollinearity was highlighted (Okina, 1986).
16
The short-term market interest rates such as the uncollateralized overnight call rate remained above theoﬃcial discount rate until June 1995 due to regulatory interest rates and the BOJ’s discount-window policy. In
July 1995, the BOJ adopted the new framework for its operating procedures where it expected the short-term
interest rates to remain below the discount rate and supplied funds to the market.

9Since the end of the 1990s, the BOJ has been compelled to manage policies under low
interest rates. The BOJ adopted the zero-interest rate policy (ZIRP) from February 1999
to August 2000, under which it lowered an overnight call rate to be almost zero. The BOJ
adopted a quantitative monetary easing (QME) policy from March 2001 to March 2006, which
is characterized as follows; it ﬁxed the overnight call rate at almost zero percent, set the target
level of the current account balances held by ﬁnancial institutions at the BOJ, and committed
to maintaining the policy until the CPI registers at a stable zero percent or an increase year-onyear. While it had returned to the use of the overnight funding rate as its instrument from March
2006, the BOJ adopted a comprehensive monetary easing (CME) policy in October 2010, underwhich it began purchasing ETFs, J-REITs, and other assets in an asset purchasing program at
an overnight call rate of 0% to reduce various risk premiums. The BOJ took monetary easing
one step further by introducing quantitative and qualitative monetary easing (QQE) policy inApril 2013. In February 2016, the BOJ took further steps, and started a negative interest rate
policy (NIRP) where it set the target level of the overnight call rate at a negative value. In
September 2016, the BOJ introduced QQE with Yield Curve Control (YCC)–a program thattargets both short-term and long-term policy interest rates.
The BOJ has been putting an eﬀort to follow the development in academia to carry out
empirical analyses which address the important features of the macroeconomy and monetary
policies in reality. Since the 1990s, empirical research at the BOJ has been conducted by incorporating standard macroeconometric models, such as Dynamic Stochastic General Equilibrium(DSGE) models, VAR models, and ﬁnance models, as well as traditional macroeconometricmodels. For example, the BOJ completed a new macroeconomic model in 2003; The Japanese
Economic Model (JEM). JEM is a DSGE-type econometric model that includes long-term equilibrium and short-term dynamics (Fujiwara et al., 2005). This has been used for economic
forecasting for policy decisions of the Bank. Additionally, the BOJ has utilized JEM and othermacroeconometric models to analyze important topical issues related to its policy decisions andin preparing its economic outlook.17
Empirical studies using econometrics methodologies are also being conducted on ZIRP and
the subsequent unconventional monetary policies by BOJ economists. For example, Ugai (2007)17

The Outlook for Economic Activity and Prices (the Outlook Report) presents the BOJ’s outlook for developments in economic activity and prices, empirically assesses upside and downside risks, and outlines its views on the
future course of monetary policy. https://www.boj.or.jp/en/mopo/outlook/index.htm/ (Accessed November
18, 2020)10

concludes in his comprehensive survey of empirical studies on QME policy from March 2001 to
July 2006, that while the QME policy contributed to lowering the yield curve, the eﬀects of QME
policy in raising aggregate demand and prices were uncertain. Speciﬁcally, Ugai (2007) classiﬁes
the eﬀects of QME policy into those on ﬁnancial variables and those on real variables. Regarding
the eﬀects of the former, Baba et al. (2005), for example, examined the eﬀects of commitment on
ﬁnancial variables by using a macro-ﬁnance model that combined a small macroeconomic model
consisting of aggregate demand and supply equations and a monetary policy rule with a term
structure of interest rates. They found that a commitment to maintaining a zero-interest rate
for a long period aﬀected the entire yield curve.18 Regarding the eﬀects of the latter in contrast,
Kimura et al. (2003) and Fujiwara (2006) examined the QME policy eﬀects on real economic
variables by using the VAR model and argue that the eﬀect of the increase in base money on
prices, GDP, and industrial production was undetectable or was small. These empirical analyses
can cause the BOJ to hesitate to inject excess reserves with ﬁxed policy rates as a means toinﬂuence macroeconomic conditions up until the policy by the BOJ governor Mr. Haruhiko
Kuroda.19
Currently, the BOJ has acknowledged that it is crucial to present empirical evidence tothe public in conveying its intentions of the policy decisions as well as its economic outlook.
In particular, regarding the QQE policy, the BOJ released a “Comprehensive Assessment” ofmonetary easing based on empirical analysis in September 2016 (the Bank of Japan, 2016). In
this report, the BOJ expressed its view on the eﬀectiveness of the reduction of real interest
rates as a main transmission channel of QQE and the causes of the sluggish inﬂation and its
expectations during the QQE period by using macroeconometric models including a new typeof JEM, Q-JEM, and its counterfactual simulation.20 Then, the BOJ decided to introduce
the YCC policy and “inﬂation-overshooting commitment” where the BOJ committed itself to18
Additionally, Okina & Shiratsuka (2004) and Oda & Ueda (2007) report that the QME policy, which committed itself to the actual movement of the CPI, had a signiﬁcant eﬀect on lowering the yield curve.
19
In fact, Mr. Masaaki Shirakawa, the former governor of the BOJ, mentioned the QME policy eﬀects on
aggregate demand and prices as follows (Shirakawa, 2010):

Empirical studies on Japan mostly show that quantitative easing produced signiﬁcant eﬀects onstabilizing the ﬁnancial system, while it had limited eﬀects on stimulating economic activity andprices.
20
The Bank of Japan (2016) pointed out that (1) the reduction of real interest rates due to QQE programs
was eﬀective in alleviating deﬂation, (2) exogenous factors, such as the decline in crude oil prices, the weakness
in demand following tax hikes in April 2014, and the slowdown in emerging economies and volatile global ﬁnancial markets have lowered the observed inﬂation, and (3) inﬂation expectations weakened because expectations
formation in Japan is largely backward-looking. See also Kan et al. (2016) and Hirakata et al. (2019) for details.

11keeping an accommodative monetary policy stance until the CPI inﬂation exceeds the pricestability target of 2 percent and stays above the target in a stable manner.

3

Japan’s conventional monetary policy

This section describes Japanese time-series data to present an overview of the macroeconomy
and monetary policies since the 1980s. Speciﬁcally, we brieﬂy summarize ﬁnal and operationaltargets for monetary policies. Additionally, we provide evidence of the stable money demand
relationship to show that the BOJ can aﬀect the money market conditions by changing shortterm policy rates in the implementation of conventional monetary policy.

3.1

Final and operational targets for Japan’s monetary policy since the 1980s

The BOJ, like the central banks in many other advanced countries, implements monetary policy to maintain price stability. Both academics and policymakers share the belief that stableprices play a critical role in enhancing both economic growth and economic stability. As Shizume(2018) reports in his review, the primary goal of Japan’s monetary policy is to maintain macroeconomic stability since the country achieved industrialization and became a leading economic
power in the 1980s.
To begin explaining the evolution of macroeconomic stability in Japan, we consider the
behavior of the output gap and inﬂation during the last 40 years. The left panel of Figure 2
shows the time series of the real GDP gap, deﬁned as a percentage diﬀerence between the actuallevel of the real GDP and the potential level estimated by the Cabinet Oﬃce from 1980 to 2019.
The right panel shows the time series of core consumer price index (CPI; all items less freshfood) inﬂation, a measure the BOJ refers to as a metric for price stability. The shaded areas
show periods of recession in Japan, as deﬁned by the Cabinet Oﬃce.
The Japanese economy rapidly expanded in the 1980s following the great inﬂation by theoil crisis. It then underperformed from the early 1990s up to the middle of the 2000s. Japan’s
real GDP level fell below its potential at the end of 1992, several years after the burst of the
bubble economy, and remained stagnant with little recovery in sight (left panel of Figure 2).
Meanwhile, other advanced countries experienced sustained economic growth under the Great
Moderation.21 The long-lasting negative or low output gap moved in step with the downward
21

The Great Moderation was a sharp reduction in the volatility of both inﬂation and output growth in developed

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Figure 2: Real GDP gap and core CPI inﬂation in Japan since the 1980s
Notes: Real GDP gap (percent, left panel) and core CPI inﬂation (percent, right panel) from January 1980 toMarch 2019. The shaded areas show periods of recession in Japan, as deﬁned by the Cabinet Oﬃce.

trend in CPI inﬂation. CPI inﬂation fell sharply from three percent at the end of 1990 to below
1 percent after 1994, and the decline persisted to the end of the decade, pausing only brieﬂy inaround 1996 (right panel of Figure 2).22 We can see that the BOJ failed to achieve its objectiveof price stability throughout this protracted downturn.
Japan’s economic recovery in the mid-2000s came to a dramatic halt with the outbreak of the
global ﬁnancial crisis in 2008. However, the economy rebounded from the downturn more quickly
than it did from the long-lasting recession after the burst of the bubble in the 1990s. Japan’s
real GDP level fell below its potential in the fourth quarter of 2008 and troughed at almost
seven percent below the potential in the ﬁrst quarter of 2009, thus showing its largest decline
since 1980 (left panel of Figure 2). The duration of the recession, however, was moderate incomparison with the great depth of the downturn. Real GDP stayed around the level of potential
after the 2nd quarter of 2009, with only two interruptions: the Great East Japan Earthquake

countries, such as the U.S., the Euro area, and the U.K., from the mid-1980s to the global ﬁnancial crisis in 2007.
In their investigation of the sources of volatility changes in economic activity in the G7, Stock & Watson (2005)
suggest that a signiﬁcant portion of the widespread reduction in volatility is associated with a reduction in
the magnitude of the common international shocks in all of the G7 economies but Japan. They ﬁnd that thecyclical ﬂuctuations in the Japanese GDP during the 1980s and 1990s are almost detached from those of otherG7 economies, with domestic shocks explaining almost all movements.
22This period is often termed as the “great recession” (Kuttner & Posen, 2001) and “lost decade” (Hayashi &Prescott, 2002).

13and tsunami in 2011 and a temporary economic downturn in 2012.2324 These features of therecession following the global ﬁnancial crisis diverge from those seen in the post-bubble recessionof the 1990s. CPI inﬂation fell sharply to less than minus two percent in mid-2009 but bounced
back to zero in the middle of 2011. This behavior suggests that the BOJ may have signiﬁcantly
contributed to early recovery from the severe economic downturn.
Next, we consider the behavior of the short-term policy rate as an operational target for
conventional monetary policy. As Okina (1993), Ueda (1993), Miyao (2002a) and others notein their institutional discussions, the BOJ has attempted to control the overnight funding ratein the interbank market (overnight call market rate).
In practice, the overnight funding rate can be considered a de facto policy instrument
through the market mechanism. Technically it is uncertain whether the overnight funding
rate can be considered a policy instrument as the lever under the BOJ’s direct control. The callmarket is the interbank market where ﬁnancial institutions transact their reserves at the BOJ.
Therefore, the call market rate is determined by a supply-demand equilibrium in the market.
This is diﬀerent from other policy instruments, such as window guidance, oﬃcial discount rate,
the interest rate on reserves, the reserve supply, and the asset purchases the BOJ directly control. However, the BOJ has a strong inﬂuencing power on the call market rate by adjusting
the reserve supply through open market operation25 and establishing the corridor by the oﬃcial
discount rate at the ceiling and the interest rate on (part of) excess reserves at the ﬂoor. The
market participants in the interbank market fully acknowledge this power and have the expectation with conﬁdence that, if the call market rate deviated from its operational target, theBOJ would intervene in the market to achieve the target. This implies that the BOJ inﬂuences
the demand in the reserve market as well as the reserve supply. As a result, it is possible for
the BOJ to indirectly control the call market rate by preventing large and persistent deviations
from the target through the open mouth operation with the help of its strong power and the
expectation, not necessarily through the open market operation.26
23

The Great East Japan Earthquake and the resulting tsunami hit the Tohoku area on March 11, 2011, which
triggered the meltdown at the Fukushima Daiichi nuclear power plant. This disaster caused serious damage overmultiple prefectures; the Cabinet Oﬃce estimated the direct ﬁnancial damage from the disaster, as of June 2011,
to be approximately 16.9 trillion yen. https://www.reconstruction.go.jp/english/topics/GEJE/ (Accessed
November 16, 2020)
24
The Economic and Social Research Institute within the Cabinet Oﬃce determined that the Japanese economy
fell into recession from May to November 2012 based on the coincident index of the business condition indicators.
25
See Hayashi (2001) and Uesugi (2002) for the empirical evidence that supports the existence of liquidity
eﬀects in the call market in Japan.
26
See Guthrie & Wright (2000) and Friedman & Kuttner (2010) for more discussion on how central banks set

14

While it has multiple policy instruments in implementing monetary policy, the BOJ has
adjusted the overnight funding rate as a key policy instrument to signal its stance of conventionalmonetary policy. In particular, the BOJ oﬃcially used the uncollateralized overnight call rate
as a policy instrument from September 9, 1998 to March 19, 2001; July 14, 2006 to October 5,
2010; and from February 2016 to the end of the sample period. On the other hand, the BOJ
often uses other instruments; The BOJ mainly used the oﬃcial discount rate from the 1970s to
September 8, 1995. The BOJ used the current account balances it holds as a QME scheme fromMarch 19, 2001 to July 13, 2006. The BOJ used the number of unconventional asset purchases
as a CME program from October 5, 2010 to April 4, 2013. The BOJ used the amount ofmonetary base and unconventional asset purchases as a QQE program from April 5, 2013 to thepresent. Also, the BOJ used the 10-year Japanese government bond yield from September 21,
2016 to the end of the sample period. In practice, however, the BOJ has generally controlled
the overnight funding rate with a great deal of precision over time. This control implies thatthe overnight funding rate can be regarded as a useful indicator of the monetary policy stancein Japan.
In fact, the overnight funding rate is a good measure of the monetary policy stance overtime. Figure 3 shows the time series of the overnight funding rate (uncollateralized overnight
call rate) in Japan from 1980 through 2019. The dark- and light-shaded areas respectively
show months that coincide with policy decisions to tighten and expand monetary conditions.
Regardless of the diﬀerent policy instruments, we can conﬁrm that the overnight funding rate
rises and falls immediately after the tightening and expanding decisions by the BOJ.
As previous studies such as Clarida et al. (1998), Bernanke & Gertler (1999), Jinushi et al.
(2000), and Shibamoto (2008) argue, the BOJ systematically changes the overnight funding
rate target in response to the economic condition. In particular, the BOJ lowered the overnight
funding rate from eight to 0.5 percent over the period from July 1991 to September 1995, thus
maintaining an accommodative policy stance in response to the economic recession following
the burst of the bubble economy. The BOJ also implemented a monetary easing policy in thewake of the global ﬁnancial crisis at the end of 2008. However, there was only a small change
in the overnight funding rate from 0.5 to 0.1 percent, with little room to cut the rate in the
low-interest-rate environment.
the policy rate.

15












  

  

  

  

         

Figure 3: Overnight funding rate in Japan
Notes: Uncollateralized overnight funding (call market) rate (monthly average, percent) from January 1980
to March 2019. We calculate it backward for the period before June 1985 using the monthly change in thecollateralized overnight call rate from February 1980 to July 1985. The dark- and light-shaded areas show themonths coinciding with tightening and easing policy decisions, respectively.

Two caveats are notable when dealing with the overnight funding rate as a measure of themonetary policy stance in Japan. First, the overnight funding rate should be regarded as ameasure of the conventional monetary policy stance. In addition to pursuing a conventional
short-term interest rate policy, the BOJ eased monetary conditions further using unconventional
policy instruments such as excess reserve injections and large-scale unconventional asset purchases since the beginning of the 2000s. Figure 3 shows that some of the changes in the policy
stance are not necessarily reﬂected in the overnight funding rate over the following periods: the
QME period from March 2001 to July 2006, the CME period from October 2010 to March 2013,
and the QQE period from April 2013 to the end of the sample period. In the next section, we
empirically assess the role of the conventional monetary policy by quantifying the causal eﬀectof the short-term interest rate policy.
Second, as Figure 3 shows, the ﬂuctuation in the short-term rate from the end of the 1990s
was relatively small compared with that before. When dealing with the macroeconomic datafrom the 1980s to the end of the sample period, we consider the speciﬁcation of the econometricmodel to account for the non-linear relationship between the macroeconomic variables and theshort-term interest rate stemming from the low-interest-rate environment.

16

3.2

Role of short-term interest rate in the money market

Conventional monetary policy plays an essential role in the money market. Central banks canmake monetary policy by managing the short-term interest rates representing money market
conditions, rather than controlling the quantity of the money stock. 27 They can allow the moneystock to ﬂuctuate in response to the current state of the economy. Given a level of operationaltargets of the short-term policy rates, the money stock can be adjusted to restore a long-run
equilibrium in the money market when unpredictable ﬂuctuations hit the economy. 28 Changes
in the interest rate target can lead to changes in the conditions for equilibrium in the money
market.
In this subsection, we provide time-series evidence in the money market related to Japan’s
monetary policy. Speciﬁcally, following Miyao (2002b), Bae et al. (2006), and Nakashima (2009),
we show some empirical results that suggest there exists a stable long-run relationship between
the short-term policy rate, the money stock, the real output, and the price level over the period
including the low-interest-rate environment.29
Although commercial banks themselves create it, central banks can inﬂuence the quantityof money stock by changing the price of money, that is, interest rates. As described in standard
textbooks in economics, such as Mankiw (2010), the liquidity preference theory allows thedemand for money held to depend on the interest rate. It is based on the assumption thathouseholds allocate their wealth either to an interest-bearing asset (bonds) or to a non-interestbearing asset (money). Since holding wealth as money requires forgoing the returns on otherassets, such as bonds, the nominal interest rate is the opportunity cost of holding money.
Consequently, by raising the cost of holding money, an increase in the nominal interest rate willlead people to economize on their cash holdings.
The liquidity preference theory suggests the following function relating the demand for27
In practice, especially since the 1990s, the BOJ has not considered monetary aggregates as an instrument
and neither as a ﬁnal or operational target for monetary policy, including the period under a low-interest-rateenvironment.
28
On the other hand, as Poole (1970) theoretically describes, if money demand is highly volatile, ﬁxing themoney stocks under the target would result in much variability in the real interest rate, and this will translate
into more variance in the real economy.
29
Miyao (2002b), Bae et al. (2006), and Nakashima (2009) ﬁnd a stable long-run relationship between the realmoney-income ratio and the log of the short-term interest rate during their sample periods, including when the
low-interest-rate period that persisted in Japan from 1995 onward is included.

17money stock, M S d , to income, Y , and (short-term) nominal interest rate SR,

M S d = P M d (Y(+) , SR(−) ),(1)

where P refers to the price level. The M d function increases with Y since a greater volumeof transactions requires more money and decreases with SR reﬂecting the tendency to moveout of money and into alternative assets such as bonds as the interest rate rises. P enters
multiplicatively which implies that changes in the price level are assumed to have a one-for-one
eﬀect on the demand for nominal money stocks. Therefore, the M d function can be interpreted
as the demand for real money stocks, M S/P . In equilibrium, money demand will be equal to
the quantity of money supplied by the banking system (M S s = M S d ).
Following Miyao (2002b), among others, we analyze the log-log speciﬁcation of an equilibrium money demand relation such that:log(M St ) − log(CP It ) = βy log(Yt ) + βsr log(SRt + 0.2) + β1 + et ,(2)

where M St , CP It , Yt , and SRt denote nominal monetary aggregates (here M2), price level(here the consumer price index for all items except fresh foods), real output (here the industrial
production index), and nominal short-term interest rate (here the overnight funding rate),
respectively. βy and βsr denote income elasticity and interest elasticity, respectively. et is themoney demand residual. In this speciﬁcation, the relationship between real money stock andshort-term interest rate is non-linear but linearized after taking the logarithm of the short-term
interest rate as well as the other variables to estimate interest elasticity βsr . We add 0.2 tothe short-term interest rate to avoid the log of a short-term rate that is too volatilely near zero
percent.30
First, we investigate whether cointegrating relation exists in the money stock, price level,
output, and the short-term interest rate.31 Speciﬁcally, we calculate the augmented Dickey &30

We obtain qualitatively similar results when we use the formula log(SRt + 0.1), log(SRt + 0.3), or log(SRt +0.4).
31
We performed the augmented Dickey & Fuller (1979) (ADF) test of the unit root process against the stationary process for the money stock, price level, output, and the short-term interest rate, and obtained empiricalresults that imply the money stock, price level, output, and the short-term interest rate can follow non-stationary
processes. When neither has a causal eﬀect on the other, a spurious relation estimated by OLS can occur due tothe presence of a stochastic trend in these variables. On the other hand, when the variables are cointegrated inthat a linear combination of the variables is stationary, the relation is not spurious.

18

Table 3: Cointegration test for money demand function

ADF Statistics
lag selected
Unknown level shift

PO test-2.09
3No

GH test
-5.83
7
Yes

Notes: The sample period is January 1971 through March 2019. ADF statistics are t-statistics for the ﬁrst order
autoregressive coeﬃcient in the autoregressive model without/with a level shift that equals to one. Critical valuesof ADF statistics for the residuals of the money demand function (2) with no level shift at the 10 percent, the 5
percent, and the 1 percent level of signiﬁcance, tabulated by Phillips & Ouliaris (1990), are −3.53, −3.84, and−4.50, respectively (PO test). Critical values of ADF statistics for the residual of the money demand function(2) with level shift at the 10 percent, the 5 percent, and the 1 percent level of signiﬁcance, tabulated by Gregory& Hansen (1996), are −4.69, −4.92, and −5.44, respectively (GH test). The lag lengths in the autoregressivemodel used in the PO and GH test regressions are chosen by general to speciﬁc reduction (from twelve lags)
proposed by Hall (1994).

Fuller (1979) (ADF) statistics for the residual of the linear regression (2), êt , and use Phillips &Ouliaris (1990) distribution to test the null of no cointegration against cointegration (PO test).
Moreover, we employ a test procedure proposed by Gregory & Hansen (1996) and calculate
the ADF statistics of no cointegration against cointegration with a possible level shift in an
unknown timing (GH test). We select the lag length in the autoregressive model used in the
PO and GH test regressions by general to speciﬁc reduction (from twelve lags) proposed by Hall
(1994).
Table 3 reports the cointegration test results. Using a PO test, we cannot detect cointegration. However, when we allow for the possibility of a level shift in an unknown timing, a GH
test detects cointegration with the logged real output and the logged overnight funding rate.
This implies that, if we take into account a possible level shift of it, there exists a cointegrating
money demand relation over the sample period even including a low-interest environment after
the second half of the 1990s in Japan, which is consistent with the ﬁndings by Miyao (2002b),
Bae et al. (2006), and Nakashima (2009).
Next, given that cointegration in money stock exists, we then investigate whether there
would indeed be a level shift in the cointegrating relation. We perform a SupF test developed
by Hansen (1992) under the null of a stable constant in the cointegrating vector against thealternative of a level shift in an unknown timing. We obtain the SupF statistics of 311.40,
which indicate that there was a signiﬁcant level shift in the cointegrating vector. Additionally,
the data point with the largest F-statistic is November 2008. These results suggest that a19

Table 4: Estimation results for money demand function in Japan

Model
β̂y

(3)
1.39
(0.01)-0.106(0.003)
4.48(0.07)
0.35(0.01)
n.a.

β̂sr
β̂1
β̂d
Controls(3)
1.38(0.04)-0.107(0.006)
4.53(0.17)
0.33(0.02)(2)
1.42(0.02)-0.111(0.011)4.37(0.08)(2)
1.38(0.04)-0.123(0.030)4.59(0.22)

12 leads and lags ofn.a.

12 leads and lags of∆ log(Yt ), ∆ log(SRt + 0.2)

Adj-R2
Sample start
Sample end

0.99
January 1970
March 2019

0.99
January 1971
March 2018

∆ log(Yt ), ∆ log(SRt + 0.2)

0.97
January 1970
March 1995

0.98
January 1971
March 1995

Notes: The numbers in parentheses are Newey & West (1987) heteroskedasticity and autocorrelation robust
standard errors for least squares with a 5-month or 7-month lag truncation for the period, January 1970 (1971)March 1995 and January 1970 (1971)-March 2019 (2018), respectively.

permanent shift in money demand has occurred after the global ﬁnancial crisis although income
and interest elasticities are stable.
Finally, we estimate the parameters of money demand relation by allowing a level shift. We
regress the following money demand function by allowing a level shift in November 2008 whichcorresponds to the data point with the largest F-statistic for structural break test:log(M St ) − log(Pt ) = βy log(Yt ) + βsr log(SRt + 0.2) + β1 + βd dt + et ,(3)

where dt refers to the dummy variable that is set to one after November 2008 and zero elsewhere.
We use the standard OLS procedure and dynamic OLS procedure developed by Stock & Watson(1993) which involves adding twelve leads and lags of the ﬁrst diﬀerence of the logged real output
and the logged overnight funding rate to obtain the estimates of the parameters together with
the Newey & West (1987) heteroskedasticity and autocorrelation robust standard errors with a
7-month lag truncation. Then, we estimate the parameters of money demand relation (2) using
the sample which ends in March 1995 when the SR was more than 2%, in order to check whether
we indeed regard the parameters for income and interest elasticities, βy , βsr , as time-invariant
even under a low interest rate.

20

Figure 4: Interest elasticity of money demand in Japan
Notes: The sample period is January 1970 through March 2019. Scatter plots of real money/output ratios(log(M St ) − log(CP It ) − βy log(Yt )) where βy = 1.38 based on Dynamic OLS estimation using sample fromJanuary 1971 to March 2018 and a log of short-term policy rates (log(SRt + 0.2)). Two dashed lines indicate
the ﬁtted value for money demand with and without a level shift (0.33) based on Dynamic OLS estimation using
sample from January 1971 to March 2018. The symbol x indicates the scatter plot for the period after November
2008.

Table 4 presents the estimation results. The estimated interest elasticity βsr is about −0.1,
which is comparable with the ﬁndings in previous studies such as Miyao (2002b), Bae et al.
(2006), and Nakashima (2009). Additionally, we turn out a substantial increase in money
demand from November 2008 at the 1% level of signiﬁcance. Figure 4 shows scatter plots ofreal money/output ratio (log(M S) − log(CP I) − βy log(Y )) and logs of short-term policy rates(log(SR + 0.2)), which can graphically conﬁrm the plausibility of the log-transformation of theshort-term interest rate.
Figure 5 displays the time series of money demand calculated using the estimated parameterswith the subsample up to March 1995 and the level shift that occurred in November 2008,
compared with that of actual money stock. As shown in the ﬁgure, stable money demandcan explain the actual behavior of money stock in Japan over time. This suggests that ourmodel applies a simple and parsimonious linear speciﬁcation of money demand using the log ofshort-term interest rate as an independent variable and suﬃciently captures the high-interest
semi-elasticity of money demand when interest rates are extremely low from 1995 onward.
21

Figure 5: Money stock and the value implied by money demand
Notes: The sample period is January 1970 through March 2019. The solid line indicates the actual behavior of
the log of money stock (log(M St )). The dotted line indicates the ﬁtted value for money demand based on OLS
estimation of (2) using data from January 1970 to March 1995 (log(CP It )+1.42 log(Yt )−0.11 log(SRt +0.2)+4.37).
Bold lines indicate the ﬁtted value with a level shift of 0.35 in November 2008 (log(CP It ) + 1.42 log(Yt ) −0.11 log(SRt + 0.2) + 4.37 + 0.35dt ). The shaded areas show periods of recession in Japan as deﬁned by the
Cabinet Oﬃce.

Our empirical results support the view that the BOJ has controlled money market conditions, not the quantity of the money stock, for a long time even in a low-interest-rate environment by setting the target level of the short-term nominal policy rate that is related to theopportunity cost of holding money. A change in the overnight funding rate has led to changes
in the conditions for equilibrium in the money market, which has inﬂuenced the quantity of themoney stock. Additionally, a small change in the funding rate has suﬃced to inﬂuence money
market equilibrium due to the highly sensitive interest and semi-elasticity of money demand ina low-interest-rate environment.

4

Measuring the eﬀect of interest rate policy

Since the work of Sims (1980), the structural VAR methodology has been widely applied to address issues when measuring the eﬀects of monetary policy in quantitative monetary economics.
As a methodology consisting of multiple equations with multiple time series, a VAR model

22

provides a systematic way to capture rich macroeconomic dynamics from a general equilibrium
perspective. It also provides a coherent approach to structural inference and policy evaluation
as well as macroeconomic forecasting.

4.1

VAR speciﬁcation

To analyze the dynamic relationship among macroeconomic variables, we construct the following
k-variable reduced-form VAR model:
Xt = a0 + A1 Xt−1 + · · · + Ap Xt−p + ut ,(4)

A(L)Xt = a0 + ut ,(5)

which can be expressed aswhere Xt is a k-by-one vector of time series variables of interest, a0 is a k-by-one constantvector, A(L) = I − A1 L − · · · − Ap Lp is a pth order lag polynomial of a k-by-k coeﬃcient matrix
Aj (j = 1, · · · , p) and ut is a k-by-one vector of serially uncorrelated innovations with a mean ofzero and a covariance matrix of Σu .
Speciﬁcally, we consider the following variables in constructing the VAR model. We let theﬁrst variable in Xt be the log of the short-term policy rate (SR) plus 0.2, which we denote
by XS,t . We use this as a measure of the monetary policy stance in the implementation of
the conventional monetary policy in the same manner as described in subsection 3.2. We
check whether our VAR, including the log-transformation of the SR, is stable over the entire
sample period. In addition to the measure of the monetary policy stance, we also include ﬁvemacroeconomic variables: three ﬁnancial market variables (the long-term rate [LR], stock priceindex [SP], and money stock [MS]), one real economic variable (the real GDP gap [GGAP]),and one price indicator (the core CPI [CPI]).32 See the Appendix for more detailed informationon the aforesaid variables in the VAR. The variables, XSP,t , XMS,t , and XCPI,t are expressed inlogarithm and multiplied by 100.
The reduced-form monthly VAR model is estimated over the period from January 1985
through January 2016. Our dataset for the VAR estimation covers the period when the BOJ
controlled the overnight funding rate mainly through open-market operations to inﬂuence the32

When we use the log-transformation of LR plus 0.2, we obtain qualitatively similar results.

23

formation of the market interest rates that started around the mid-1980s. 33 We omit the period
since February 2016 where the BOJ implemented the NIRP.34
Following Friedman & Kuttner (1992), for instance, the lag length p in the reduced-form
VAR estimation is set to twelve. We conﬁrm that taking twelve lags is suﬃcient to capture thesystem dynamics.35
As described above, we check the possibility of a structural shift in the dynamics of our model
before proceeding with the estimation of the eﬀects of monetary policy. From the end of the
1990s, in particular, the BOJ implemented not only conventional policy but also unconventional
policies such as the ZIRP (in February 1999) and the QME policy (in March 2001). In Septemberof 2008, the global ﬁnancial crisis sent the Japanese economy into a severe economic downturn.
These episodes induced a signiﬁcant structural shift in the dynamic system in our VAR model.
To test the structural stability of the VAR model, we conﬁrm whether any structural break
can be found in the parameters of our reduced-form VAR system deﬁned by (5). The procedure
tests whether the overall parameter values are unchanged between the two periods before andafter a given possible break date. Thus, we test the null hypothesis that all of the model
parameters are the same, as against the alternative of a structural shift. We begin by estimating
the following reduced-form system:

A(L)Xt + Ad (L)Xt dt = a0 + ad0 dt + vt ,(6)

where dt is a dummy variable set to one after a given break date, ad0 is a k-by-one constantvector, Ad (L) = I − Ad1 L − · · · − Adp Lp , and vt is a vector of innovations with a mean of zeroand a covariance matrix of Σv . The test statistics are (T − 2(kp + 1))(log |Σu | − log |Σv |), where
T is the number of observations. Under the null hypothesis, ad0 = 0, Ad1 = Ad2 = · · · = Adp = 0,33

Itoh et al. (2015) described the situation in detail using materials from the BOJ Archive. In the mid-1980s,
the BOJ fully recognized the need to reform monetary policy implementation due to the progress in ﬁnancial
deregulation toward the eﬀective use of freely determined market interest rates.
34
The BOJ decided to adopt a NIRP on January 29, 2016, cutting the short-term policy rate by 20 basis points
into negative territory. It charged banks 0.1 percent of a small portion of their excess reserves on February 16,
which incurred an additional and direct cost for banks. This situation brought concerns regarding a substantial
diﬀerence in the monetary transmission of interest rate changes between the conventional monetary policy andthe negative interest rate policy.
35
The Bayesian information criterion selects three lags, and the Akaike information criterion selects ﬁve lags.
We perform a modiﬁed likelihood ratio test proposed by Sims (1980) to check whether taking three or ﬁve lags
is suﬃcient. The chi-square statistics indicate that the null hypothesis of three or ﬁve lags is rejected at theone percent signiﬁcance level against the alternative of 12 lags. However, they also indicate that conventionalsigniﬁcance levels do not reject the null of 12 lags, as against the alternative of 13 lags. Moreover, we conﬁrm
that the estimated impulse responses are insensitive when more than 12 lags are used.

24the test statistic is asymptotically chi-squared with degrees of freedom equal to k(kp + 1).
Table 5 shows the stability test results reporting chi-squared statistics with p-values. The
following are set as possible break dates: February 1999, when the BOJ launched the ZIRP;March 2001, when the BOJ launched the QME; and September 2008, when the Lehman Brothers
Bankruptcy triggered the global ﬁnancial crisis. The chi-squared statistics in this table indicatethat conventional signiﬁcance levels do not reject the null of structural stability, as against thealternative of structural shifts. The test results suggest that our VAR model is stable throughout
the entire sample period.
Table 5: Stability test results for the VAR model
Break date
Test statistics

February 1999
472.97[0.12]March 2001
464.20[0.19]

September 2008
458.85[0.24]

Notes: This table reports the chi-squared statistics that tests the null hypothesis, which states that the overall
parameters of the reduced-form VAR system deﬁned by (5) are unchanged between the two periods before andafter a given break date (shown in the ﬁrst row of the table) when estimated over the period from January 1985
to January 2016. The numbers in brackets are p-values for the test.

Figure 6 shows the prediction values of X using the estimated parameters from January 1985
to January 1999 (before ZIRP), such that X̂t85−99 = â85-99+ Â85-99
Xt−1 +· · ·+ Â85-99
Xt−p . Fromp0
1
February 1999 to January 2016, X̂t85-99 is the one-step forecast conditioning on Xt−1 , · · · , Xt−pbased on the estimated parameters â85-99, Â(L)85-99 from January 1985 to January 1999 (before
0
ZIRP was launched). We ﬁnd that the predicted values in Figure 6 suﬃciently explain thetime-series pattern of the short-term policy rate and other macroeconomic variables from 1999,
although the prediction of the long-term rate over-ﬂuctuates to a slight degree. This result
suggests that the proposed VAR model and the chosen parameters remain stable over the
sample period.

4.2

Identiﬁcation of the causal eﬀects of the interest rate policy

When quantifying the monetary policy eﬀect, we examine whether the policy-induced changes in
the monetary stance have an impact on the economy. A central bank, however, systematicallychanges its policy stance in response to economic conditions. Therefore, we must distinguishbetween the policy-induced changes and systematic changes from the observed policy stance ininvestigating the causal eﬀect of monetary policy.
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Figure 6: Financial market, output gap, and inﬂation in Japan
Notes: Sample period from January 1985 to January 2016. Solid lines: actual values. Dotted lines: one-step
VAR forecasts conditional on Xt−1 , · · · , Xt−12 , which is based on the estimated parameters over the sample fromJanuary 1985 to January 1999. The shaded areas show periods of recession in Japan as deﬁned by the Cabinet
Oﬃce. SR: short-term policy rate, LR: long-term rate, SP: stock price, MS: money stock, GGAP: GDP gap,
CPI: core consumer price index.

Let us consider the monetary policy reaction function to understand better how a central
bank systematically adjusts its policy stance in response to various elements in a macroeconomy. On the premise that the current and lags of Xt include suﬃcient information on themacroeconomy, the reaction function is expressed as follows:
XS,t = f (Xt-S , Xt−1 , · · · , Xt−p ) + ϵM P,t .

(7)

The function f (Xt-S , Xt−1 , · · · , Xt−p ), where Xt-S is a subvector of Xt derived with XS,t dropped,
represents how the policy stance measure XS,t systematically responds to the information set.
2
The random variable ϵM P,t with a mean of zero and a variance of σM
P represents the nonsys-

tematic component of the reaction of XS,t , which is often referred to as a “monetary policy
shock.” In interpreting the policy shocks, we can speculate that (1) ϵM P,t reﬂects emergent
changes in the preferences of monetary policy authorities, where the BOJ decides to get toughon macroeconomic conditions; or (2) monetary policy authorities contemplate in retrospect ofmaking something it should not have, where the BOJ keeps its policy stance even if the economy
worsens.36
36

See Christiano et al. (1999) for more detail on the interpretation of monetary policy shocks.

26

A key question in the literature is how to identify monetary policy shocks to measure thedynamic causal eﬀects of monetary policy. Several studies (e.g., West (1993); Shioji (2000);
Miyao (2002a); Nakashima (2006); and Braun & Shioji (2006)) estimate the macroeconomic
eﬀects of monetary policy shocks in Japan by developing VAR models with internal restrictions, such as the short-run restrictions proposed by Sims (1992), Bernanke & Blinder (1992),
Bernanke & Mihov (1998), and Christiano et al. (1999) and the sign restrictions proposed by
Uhlig (2005). West (1993) adopts the non-recursive identiﬁcation framework in conjunction
with the innovations in the money stock (M2) equation as monetary policy shocks in Japan.
Shioji (2000) uses the non-recursive identiﬁcation framework with several monetary policy variables (the BOJ loan and high-powered money).37 Miyao (2002a) argues that the institutionalfeatures of the BOJ’s operating procedures under the conventional policy setting ﬁt a recursive
identiﬁcation approach that is similar to that of Bernanke & Blinder (1992), Sims (1992), andChristiano et al. (1999), where disturbances in the short-term interest rate equation are viewed
as the policy shocks and the interest rate is placed before monetary aggregates in the ordering. Nakashima (2006) develops the structural VAR approach proposed by Bernanke & Mihov(1998) to model the Japanese reserve market with the BOJ’s operation procedures from January1975 to June 1995, a period when the overnight funding rate remained above the oﬃcial discount rate.38 Braun & Shioji (2006) use two alternative sign restrictions, one where a surprisetightening in monetary policy increases the short-term nominal interest rate and lowers output,
prices, and monetary aggregates, and another where it lowers the short-term nominal interest
rate and lowers output, prices, and monetary aggregates.39 Others, such as Rudebusch (1998)
and Nakamura & Steinsson (2018), however, note that serious concerns may arise regarding thereliability of the identifying restrictions internally imposed in the VAR model.
Following the argument by Rudebusch (1998) and Nakamura & Steinsson (2018), we attempt
to identify conventional monetary policy shocks in Japan using an external instrument fromthe VAR system that captures exogenous variation in the policy stance. Here, we follow themethodology developed by Stock & Watson (2012, 2018).
37

Shioji (2000) concludes that Japanese monetary policy is better described when the model assumes thatthe central bank controls the total amount of high-powered money rather than only a speciﬁc component ofhigh-powered money.
38
Until June 1995, Nakashima (2006) argues that the BOJ aimed to stabilize the overnight funding rate using
not only open market operations but also discount window lending.
39
Under the assumption where a surprise tightening in monetary policy lowers the short-term nominal interest
rate and lowers output, prices, and monetary aggregates, Braun & Shioji (2006) argue that monetary policy haslarge and persistent eﬀects on the economy.

27

In a structural VAR, the innovations ut in (5) are assumed to be linear combinations of the“structural” shocks:
ut = Θϵt ,(8)where ϵt is a k-by-one vector of structural shocks, which are assumed to be independent of eachother, and Θ represents the impact matrix for the responses of the VAR variables Xt to theshocks ϵt . (5) and (8) yield a moving average representation regarding the structural shocks asfollows:
Xt = b0 + B(L)Θϵt ,(9)where b0 = A(L)−1 a0 and B(L) = A(L)−1 . Let ϵM P,t and ϵ-M P,t be a monetary policy shock
and a vector of the structural shocks other than the policy shock, respectively, such that ϵt =′′(ϵM P,t , ϵ-M P,t ) . Moreover, let Θ1 represent the impact vector for the responses of the VAR
variables Xt to the policy shock and Θ-1 represent the impact matrix for the responses of theVAR variables Xt to the other shocks, such that Θ = (Θ1 , Θ-1 ). We can express impulse response
functions of Xt to a monetary policy shock ϵM P,t as B(L)Θ1 .
′To identify Θ1 = (Θ11 , · · · , Θk1 ) , we adopt the approach proposed by Stock & Watson(2012, 2018). Suppose there is an instrument zt for the policy shock that satisﬁes the following
conditions:
E[ϵM P,t zt ] ̸= 0, and(10)

E[ϵ-M P,t zt ] = 0.

(11)

Assume also that a unit increase in ϵM P,t increases XS,t by one unit Θ11 = 1. We use theinstrumental variable (IV) regression to yield a consistent estimate of Θ i1 :-M Pui,t = Θi1 u1,t + ϵi,t,(12)

P =Θ ϵusing the instrument zt , where ϵ-M
i-1 -M P,t .
i,tThe instrument zt is the change in the three-month euro-yen TIBOR futures on the monetary
policy board meeting (MPM) dates as a proxy of the unanticipated changes in the short-term
policy rate. Speciﬁcally, up to December 1997, when the BOJ had yet to announce the MPM
dates oﬃcially, we use the daily series of surprise change measures provided by Honda and
28

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         

Figure 7: Monthly measure of unanticipated changes in the three-month euro-yen TIBOR
futures on the monetary policy board meeting dates, zt
Notes: Sample period from July 1989 to January 2016. The dark and light shaded areas show the month oftightening and easing policy decision, respectively.

Kuroki (2006, Table 1).40 From January 1998, when the BOJ started to announce the MPM
dates oﬃcially, we calculate the changes in euro-yen futures over two-day periods when the
BOJ made policy decisions (from the days before the public statements to the days after thestatements) to duly cover both the timing of the public statements and the time it took for thenews to be suﬃciently recognized, as in Ueda (2012) and Nakashima et al. (2019). We thensum up the daily series to construct unanticipated monthly changes in the short-term policy
rate. Figure 7 plots our calculation of the monthly measure of unanticipated changes in thethree-month euro-yen TIBOR futures on the MPM dates, zt , over time. To verify the robustnessof the proposed instrument, we compute the F -statistic from the ﬁrst-stage regression of the
VAR innovation of the policy stance measure u1,t on the instrument zt .41
The IV regression estimating the impact eﬀect of the policy shock is estimated over theperiod from July 1989 to January 2016.42 The starting date of our sample for the IV regression
40

Honda & Kuroki (2006) identify the event dates of the policy rate target changes and the target levels (orranges) by carefully reading the articles in the Nikkei News Paper. They then measure the changes in three-month
TIBOR futures over the events as the surprise components of the policy rate.
41
One may be concerned about a structural break of the validity of the external instrument for identifying
monetary policy shocks. In particular, unanticipated policy change measures before and after 1998 seem tobehave diﬀerently as the BOJ has implemented a low-interest rate policy since 1998; the variations in the policy
rate themselves were relatively small compared with those before. However, we conﬁrm the stability in theﬁrst-stage regression of the VAR innovation of the policy stance measure u1,t on the instrument zt ; we do notstatistically detect a structural break in the regression coeﬃcient after January 1998.
42
The reason why we employ monthly estimations is that we identify the monetary policy shocks in the VAR
model as rigorously as possible, in addition to the availability of ﬁnancial market data and CPI. The monetary29

is the day the instrument variable, zt , becomes available.

4.3

Estimation of the dynamic causal eﬀects of the change in the short-term
policy rate on the macroeconomy

The estimated impulse response functions are summarized in Figure 8. The instrument isstrong, with a heteroskedasticity-robust ﬁrst-stage F -statistic of 10.44.43 As described above,a monetary policy shock is normalized to produce a one-unit increase in the log of the shortterm policy rate (SR) on impact. The solid line indicates the estimated response for each of
the variables in levels up to 60 months. The shaded areas denote one-standard-error bands
calculated using 1000 bootstrap samples.
Overall, this ﬁgure shows that the Japanese monetary policy has a plausible eﬀect, as compared to the monetary policy eﬀects discussed in the macroeconomic literature by Boivin et al.
(2011), for instance. Figure 8 shows that the tightening monetary policy shock leads to apersistent decrease in the GGAP and the CPI. GGAP declines to its lowest values (about 3.5
percent) approximately two years after a monetary tightening and gradually works its way backup to zero.44 The CPI price stays ﬂat for roughly one and a half years due to price rigidity then declines steadily for two years to its minimum (about 1.5 percent), with long-lasting
eﬀects. The monetary tightening, meanwhile, leads to an immediate reduction in the SP, an
immediate increase in the LR, and a persistent fall in the MS. These responses imply that the
BOJ stimulates the ﬁnancial market and macroeconomy through a policy-induced change inthe short-term policy rate.

5

Policy evaluations based on counterfactual simulations

This section employed our econometric model to empirically assess cases where the BOJ conducted expansionary monetary policy in response to economic conditions. Speciﬁcally, we conpolicy surprises we use as an external instrument to identify the shocks in the VAR model randomly occur atmonthly frequencies because the BOJ decides on policies at monetary policy board meetings which were held
once or twice a month, at least, until the end of our sample period. In order to identify the shocks using themonetary policy surprises in the manner as (12) and estimate the dynamic causal eﬀect of the shocks, B(L)Θ1 ,it should be more appropriate to use monthly estimations, rather than quarterly estimations.
43
To be conﬁdent that a weak instrument problem is not present, Stock et al. (2002) and Stock & Yogo (2005)
recommend the rule of thumb that requires that the F -statistic from the ﬁrst stage regression of the two-stage
least squares should exceed ten.
44
When we use the log of the index of industrial production as a proxy of real output instead of the real GDP
gap, we obtain qualitatively similar results.

30

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Figure 8: Estimated responses to a monetary policy shock
Notes: The monetary policy shock increases the log of the short-term policy rate by one unit. The instrument forthe monetary policy shock is the monetary policy surprise measures compiled by Honda & Kuroki (2006) from
July 1989 through December 1997 and the changes in the three-month euro-yen TIBOR futures before and after
the BOJ’s policy decisions over the two-day period (from the day before the public statement to the day after thestatement) from January 1998 through January 2016. Estimation samples span January 1985 through January
2016 for the VAR and July 1989 through January 2016 for the monetary policy shock IV regression. The solidlines represent the point estimates of the impulse responses to a monetary policy shock. All the responses, except
those of the log of the short-term policy rate, are expressed in percentage points. The shaded areas denote one
standard error bands calculated using 1000 bootstrap samples. We set the lag length to twelve in the reduced
form VAR estimation. SR: short-term policy rate, LR: long-term rate, SP: stock price, MS: money stock, GGAP:GDP gap, CPI: core consumer price index.

ducted ex-post assessments of policy responses to two extraordinary shocks: the burst of the
bubble economy and the outbreak of the global ﬁnancial crisis.
Following Bernanke et al. (1997), Sims & Zha (2006), and Eberly et al. (2019), for instance,
we use VAR models to conduct counterfactual simulations to assess how the Japanese economy
would have performed under alternative monetary policies.4546 Speciﬁcally, we measure the45

An alternative approach is to use New Keynesian DSGE models (e.g., Bernanke & Gertler, 1999; Fujiwara
et al., 2005; Kan et al., 2016; Hirakata et al., 2019), which have gained popularity during the past two decades.
In this study, we use a more traditional econometric approach.
46
Our counterfactual simulations do not necessarily meet the Lucas (1976) critique; if the central bank changed
the systematic policy reactions, decision rules of the private sector would vary systematically with changes in thepolicy, and thus any change in policy would systematically alter the structure of traditional macro-econometricmodels. Our simulations are conducted under a situation where the systematic policy reaction, as well as thedecision rules of the private sector, should be the same as the actual and the counterfactual policy paths which
are derived by deviations from the systematic policy action. This implies that the private sector has no incentive
to change its decision rules because the systematic policy reaction is invariant under the counterfactual policy
paths. However, as Bernanke et al. (1997) describe, our simulations are not completely invulnerable to the Lucas

31

counterfactual values of the variables in the VAR model by taking four steps. In step 1, weCScompute the diﬀerence between a speciﬁed path of the policy stance measure X̃S,t+hand its
CS −X
actual path XS,t+h , such that X̌S,t+h = X̃S,t+h
S,t+h for h = 0, · · · , H. In step 2, we computethe sequence of monetary policy shocks {ϵ̌M P,t , ϵ̌M P,t+1 , · · · , ϵ̌M P,t+H } where the policy stancemeasure explained by the current and lags of the policy shocks (i.e., the ﬁrst element of a vectorof B̂(L)Θ̂1 ϵ̌M P,t+h ), is equal to X̌S,t+h for all h = 0, · · · , H, given ϵ̌M P,t−1 = ϵ̌M P,t−2 = · · · = 0.
In step 3, we compute the ensuing contribution of the sequence of monetary policy shocks:
X̌t+h = Â1 X̌t+h−1 + · · · + Â12 X̌t+h−12 + Θ̂1 ϵ̌M P,t+h , for h = 0, · · · , H,

(13)given a0 = 0 and the initial value X̌t−1 = X̌t−2 = · · · = X̌t−12 = 0. In step 4, we add
CS = X̌
this contribution X̌t+h to the observed value of Xt+h , such that X̃t+ht+h + Xt+h for

h = 0, · · · , H.47
For comparison, we also consider the counterfactual values of the variables in the VAR modelin the case where no structural shocks take place (ϵt+h = 0 for all h = 0, · · · , H) over the periodof the counterfactual simulation:
-ϵ
-ϵ
-ϵ
X̃t+h
= â0 + Â1 X̃t+h−1+ · · · + Â12 X̃t+h−12
for h = 0, · · · , H,

(14)

-ϵ , · · · , X̃ -ϵgiven the initial value X̃t−1
t−12 is equal to their actual values. This reﬂects the coun-terfactual path of the policy rate and the economy conditioning on the information up to themonth before the beginning of the period of the simulation. In addition to the actual path, we
use this as baselines to quantitatively assess alternative monetary policies.

5.1

More aggressive policy rate cuts after the bubble collapse in the 1990s

The Japanese economy suﬀered from a sustained recession from the burst of the bubble economy
at the beginning of the 1990s onwards, even though the BOJ lowered the policy rate repeatedly.
Japan’s GDP gap declined by more than ﬁve percent during the recession over the half-decadefrom February 1991 (the starting date of the recession phase just after the bubble collapse in thecritique because the private sector might change its expectation for policy rule and its decision rules when it
repeatedly has seen deviations from the reaction.
47
Note that our counterfactual simulations rely on ex-post data that were unavailable to the BOJ in real time.
It might, therefore, be diﬃcult to say that the BOJ should have or could have made diﬀerent decisions in real
time.

32

1990s). Moreover, the CPI inﬂation gradually declined to almost zero percent year-on-year in
1995, from three percent before the recession. The BOJ cut the oﬃcial discount rate nine times
in July 1991, November 1991, December 1991, April 1992, July 1992, February 1993, September
1993, April 1995, and September 1995. The short-term policy rate fell drastically from eight to
0.5 percent during this period, albeit at a slow pace. Some studies, such as Bernanke (2000),
Jinushi et al. (2000), and Kuttner (2014), argue that the policy rate cuts in the ﬁrst half of the
1990s were not as expansionary as they would appear at ﬁrst glance.48
Our ﬁrst counterfactual simulation supposes that the BOJ had implemented more aggressive
policy rate cuts, as compared with its own policy rule implied in the VARs, in reaction to theeconomy’s deterioration after the burst of the bubble economy in the ﬁrst half of the 1990s.
Speciﬁcally, we deﬁne more aggressive policy rate cuts at the beginning of the 1990s as thefollowing policy rate path: the actual value in June 1991 (about eight percent) to seven percent
in July 1991, six percent in November 1991, ﬁve percent in December 1991, 3.5 percent in April
1992, two percent in July 1992, 1.5 percent in February 1993, and 1 percent in February 1993.
This situation is comparable to the measures taken by the Federal Reserve (Fed) in responseto the recession in the U.S. (e.g., from 8.25 to 3 percent from June 1990 to September 1993,
from 6.5 to one percent from December 2000 to June 2003, and from 5.25 percent to almost
zero percent from August 2007 to December 2008).
The simulation results are summarized in Figure 9. The ﬁgure depicts the actual (solid lines)
and counterfactual paths of the following macroeconomic variables with the alternative policy(i.e., more aggressive policy rate cut (dotted lines), and those with no monetary policy shocks(dashed lines)): the short-term policy rate, the 12-month growth of stock price, the 12-month
growth of money stock, GDP gap, and the 12-month rate of CPI inﬂation from February 1991 toFebruary 1996. The upper-right panel in this Figure plots the actual (solid lines) and simulated
values (dotted line) of monetary policy shock ϵM P,t+h for h = 0, · · · , H, which is implied by thecounterfactual paths with more aggressive policy rate cuts shown in the upper-left panel.
It is essential for a central bank to consistently shift to an immediate and massive stimulus
to achieve price stability in response to any serious decline in real output and falling inﬂation
48
Bernanke (2000) show that money growth was weak in the ﬁrst half of the 1990s despite the large reduction of
the broader nominal interest rates. Jinushi et al. (2000) show that the Japanese monetary policy actions during
the ﬁrst half of the 1990s were delayed compared with their “good” Taylor-style policy rule, which was estimatedover the pre-bubble period from 1975–1985. Kuttner (2014) shows that the actions were not as decisive as themeasures taken by the Fed during the recessions beginning in 2000 and 2007.

33

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Figure 9: Counterfactual simulation of macroeconomic variables: More aggressive policy ratecuts at the beginning of 1990s
Notes: Solid lines indicate actual values. Dotted lines indicate simulated values obtained by setting the simulatedmonetary policy shocks so that the simulated short-term policy rate follows the more aggressive policy ratecut. Dashed lines indicate simulated values in the case without monetary policy shocks over the period of thecounterfactual simulation (February 1991 to February 1996). The shaded areas show periods of recession in
Japan, as deﬁned by the Cabinet Oﬃce. SR: short-term policy rate, SP: stock price, MS: money stock, GGAP:GDP gap, CPI: core consumer price index.

due to an extraordinary economic shock. Figure 9 shows that with more aggressive policy ratecuts from eight to one percent for two years, the GDP gap would not have fallen below zeroand risen after the recession period. CPI inﬂation would have declined only mildly and stably
remained above one percent after the recession. Financial conditions such as the stock priceand money growth would have recovered from the severe setback triggered by the burst of the
bubble economy in the middle of the recession period. These results suggest that the BOJ’s
reluctance to cut policy interest rates in response to the post-bubble downturn signiﬁcantly
contributed to the long-lasting recession of the 1990s.

5.2

No policy rate cuts after the global ﬁnancial crisis

After the outbreak of the global ﬁnancial crisis in 2008, the BOJ was forced to take prompt
actions to ensure price and ﬁnancial market stability. Japan was hit hard by the global ﬁnancial
crisis. The Japanese stock market began to fall sharply after a drop in the U.S. market, and the
GDP gap decreased dramatically with severe deﬂation. The BOJ cut the interest rate from 0.5
to 0.3 percent on October 31, 2008 and decreased further to 0.1 percent on December 19, 2008,34

albeit with some confusion and concerns that the small rate cuts would have limited impact orthe extremely low-interest rate would cause ﬁnancial markets to malfunction (Minutes of theMonetary Policy Meeting on October 31, 2008 and December 18 and 19, 2008).49 Considering
the serious damage to the macroeconomy, the ensuing recovery was achieved quite quickly.
Our second counterfactual simulation supposes that the BOJ implemented no policy ratecuts, even though its own policy rule implied in the VARs should have reacted to the economic
downturn after the outbreak of the global ﬁnancial crisis at the end of 2008. Figure 10 shows theactual and simulation values of the macroeconomic variables from December 2007 to December
2012.
Even small policy rate cuts in a low-interest-rate environment contribute signiﬁcantly to theeconomic recovery. Figure 10 shows that, apart from policy rate cuts from 0.5 percent to 0.1
percent, the GDP gap would have ranged between minus four percent and minus eight percent
even after the recession period, and the CPI inﬂation would have been persistently negative.
Financial conditions would have been more serious than they were: the stock price would havecontinuously declined. In particular, small changes in the policy rate suﬃciently inﬂuenced
the money market in line with a highly sensitive interest semi-elasticity of money demand ina low-interest-rate-environment as shown in subsection 3.2; the money growth would also havecontinuously declined, while the actual money growth was stable at around 2%. These results
suggest that if the BOJ had hesitated in cutting the interest rate in response to the recession
triggered by the global ﬁnancial crisis, the period of recession and deﬂation would have lasted
longer than it did.50

6Concluding remarks

Monetary authorities and economic researchers have spent a great deal of eﬀort to understandthe role of monetary policy in the real world. If we are to assess monetary policy empirically,49

On October 31, 2008, a proposed policy rate cut from 0.5 to 0.25 percent was defeated by three votes to ﬁve.
A vote on an ensuing proposal for a policy rate cut from 0.5 to 0.3 percent was equally split but approved in a
ﬁnal decision by Governor Shirakawa. On December 19, 2008, one of the policy board members dissented froma proposal for a policy rate cut from 0.3 percent to 0.1 percent. He was concerned that if the policy interest
rate was reduced to 0.1 percent, the resulting elimination of the spread between the policy rate and interest rateapplied to the complementary deposit facility would be likely to degrade the functioning of the market mechanism
substantially.
50
Although this study focuses on the role of conventional monetary policy, this exercise does not indicate that
unconventional monetary policies, such as the large-scale unconventional asset purchases, independent from thetarget level of the short-term policy rate, are ineﬀective. The empirical evaluation of unconventional monetary
policies following the global ﬁnancial crisis is left for future research.

35     ! " #  $ %# & $

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) 



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27
 
*+,  %  16 " 1 !  "   '  (    





)



)
)
)





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

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4 4 52 



)
)
)
)

)
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      

Figure 10: Counterfactual simulation of macroeconomic variables: No short-term policy ratecuts after the global ﬁnancial crisis, starting from November 2008
Notes: Solid lines indicate actual values. Dotted lines indicate simulated values obtained by setting simulatedmonetary policy shocks that keep the simulated short-term policy rate at the October 2008 level (about 0.5
percent). Dashed lines indicate simulated values in the case without monetary policy shocks over the period of
the counterfactual simulation (December 2007 to December 2012). The shaded areas show periods of recession in
Japan, as deﬁned by the Cabinet Oﬃce. SR: short-term policy rate, SP: stock price, MS: money stock, GGAP:GDP gap, CPI: core consumer price index.

we have three tasks to pursue: describing and summarizing the available macroeconomic data,
quantifying what we know or do not know about the true structure of the economy, and exploring the advice for policymakers in implementing monetary policy. Even when we have suﬃcient
data of interest for our purposes, we still face a great challenge in specifying an empirical modelthat appropriately captures the important features of the macroeconomy and monetary policy.
The research methods applied in the literature to overcome the diﬃculties in empirically assessing monetary policy have steadily progressed. This study describes the historical backgroundleading up to the development of empirical research on monetary policy by reviewing several
early empirical studies of Japan’s monetary policy that aﬀected the actual policies. We havealso assessed the eﬀect of Japan’s monetary policy, applying the standard empirical methodology from the present-day literature to discuss the importance of the major role of monetary
policy in developed countries (i.e., macroeconomic stability).
Our empirical analyses, using data from the 1980s onwards, attest to the eﬀectiveness ofJapan’s monetary policy despite Japan’s experience with a series of accommodative actions anda protracted weakness in economic performance. Causal inferences on the policy transmission

36

show that a policy-induced change in the short-term interest rate has a statistically signiﬁcant
impact on the ﬁnancial market and macroeconomy. Our policy evaluations also critically touchupon the crucial role of monetary policy in supporting economic growth. Our counterfactual
simulations indicate that (1) the central bank should consistently shift to an immediate andmassive stimulus to achieve price stability in the medium and long term in response to thesevere decline in real output and falling inﬂation caused by the extraordinary economic shocks.
Moreover, (2) even small policy rate cuts in a low-interest-rate environment make signiﬁcant
contributions to economic recovery.
There are several limitations and restrictions of the empirical analysis of monetary policyin this study, thus leaving room for future research that could extend into several directions.
Although we specify a benchmark VAR model similar to existing models in the literature, themodel disregards the role of commercial banks in the macroeconomy and monetary policy. Given
the history of empirical studies on monetary policy, we observe an undesirable black-boxing of
the banking sector. As theoretical studies such as Gertler & Kiyotaki (2011) show, the eﬀects ofeconomic shock and monetary policy can be ampliﬁed through their impacts on bank balance
sheets because of credit frictions. Further, the Japanese economy seemed to show non-linear
interest rate policy eﬀects through the banking sector after the BOJ implemented the NIRP. 51
An important task for future empirical research will be to reveal the dynamic interaction andfeedback eﬀects between banking variables and macroeconomic variables.
This study merely estimated the eﬀect of the changes in current short-term policy rate.
For future research to deepen our understanding of the role of monetary policy, we will haveto evaluate monetary policy by changing the future path. Today’s central banks actively use
communication tools such as forward guidance to manage expectations about the future courseof monetary policy. While policymakers hope to use forward guidance as a tool for monetary
policy, especially under the lower bound on the short-term policy rate, little is known about its
eﬀectiveness.
A ﬁnal important task for future research is to assess the eﬀectiveness of unconventionalmonetary policy. While the actual implementation of the unconventional monetary policy inmany advanced countries has stimulated empirical research on policy eﬀects, the eﬀects on

51
Brunnermeier & Koby (2018) theoretically show the existence of a reversal rate of monetary policy at whicha policy rate cut becomes contractionary for bank lending.

37the real economy are still disputable.52 Making causal inferences for an unconventional policy
is particularly confounding in this respect.53 Though beyond the scope of this study, suchinferences are worthy of future research.

Conﬂict of interest
On behalf of all authors, Masahiko Shibamoto states that there is no conﬂict of interest.

Appendix
Variable Deﬁnitions
 ODR: Oﬃcial discount rate (end of month, %); monthly series retrieved from the Bank ofJapan statistics.
 MB: Monetary base; seasonally adjusted series (monthly average, 100 million yen); monthly

series retrieved from the Bank of Japan statistics.
 SR: Short-term policy rate; uncollateralized overnight call rate (monthly average, %);monthly series retrieved from the Bank of Japan statistics for the period from July 1985
to March 2019 and calculated backward for the period before June 1985 using the monthly
change in the collateralized overnight call rate; retrieved from the Bank of Japan statistics
for the period from January 1955 to April 2016.
 MS: M2 money stock; seasonally adjusted series (monthly average, 100 million yen); re-trieved from HAVER ANALYTICS.
 Y: Index of industrial production, mining and manufacturing, seasonally adjusted series(2015=100), retrieved from NIKKEI NEEDS FINANCIAL QUEST.
 SP: Stock price; Nikkei Stock Average (Nikkei225) index (end of month, May 16,1949=100);

retrieved from HAVER ANALYTICS.
 BCREDIT: Bank credit to the private non-ﬁnancial sector; quarterly series retrieved fromthe BIS statistics, Table F2.6 (billion yen).
52

See Ugai (2007) and Joyce et al. (2012) for a survey of the empirical research on unconventional policy eﬀects.
See Nakashima et al. (2019) and studies cited therein for more details on the methodology used to identify
the eﬀect of unconventional monetary policy.
53

38

 PGDP: GDP deﬂator; National accounts statistics compiled according to 1968 SNA (1990=100)

from the 1st quarter of 1955 through the 1st quarter of 2001 and 1993 SNA and 2008 SNA(2011=100) from the 1st quarter of 1980 through the 1st quarter of 2019 (Data prior to
1994 using the 1993 SNA series), ﬁxed-based method (1968 SNA) and chain-linked method(1993 SNA and 2008 SNA), seasonally adjusted quarterly series retrieved from HAVERANALYTICS.

 RGDP: Real GDP; National accounts statistics compiled according to 1968 SNA (bench-

mark year=1990) from the 1st quarter of 1955 through the 1st quarter of 2001, and 1993
SNA and 2008 SNA (benchmark year=2011) from the 1st quarter of 1980 through the 1stquarter of 2019 (Data prior to 1994 using the 1993 SNA series), ﬁxed-based method (1968
SNA) and chain-linked method (1993 SNA and 2008 SNA), seasonally adjusted quarterly
series retrieved from HAVER ANALYTICS (billion yen).
 GGAP: Real GDP gap; quarterly series retrieved from the Cabinet Oﬃce (%); Interpo-

lated (using linear interpolation under the constraint that the average of three monthly
observations within a quarter must equal the quarterly series) to obtain monthly observations.
 CPI: Consumer price index for all items less fresh foods (2015=100); consumption-tax-

adjusted for the period from April 1989 to March 1990, April 1997 to March 1998, andApril 2014 to March 2015; retrieved from the Ministry of Internal Aﬀairs and Communications; Seasonally adjusted series were obtained using the Census X-12.
 LR: Long-term rate; 10-year Japanese government bond yields (end of month, %); retrieved

from NIKKEI NEEDS FINANCIAL QUEST.

39

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