DP2019-09

Identifying Quantitative andQualitative Monetary Policy Shocks
Kiyotaka NAKASHIMA
Masahiko SHIBAMOTO
Koji TAKAHASHI

Revised March 31, 2023

Identifying Quantitative and Qualitative Monetary
Policy Shocks*
Kiyotaka Nakashima†

Masahiko Shibamoto ‡Koji Takahashi§

March 2023
Abstract
This paper proposes a method for identifying quantitative and qualitative monetary
policy shocks in the balance sheet operations of a central bank in VAR analysis. The
method is agnostic and flexible as it relies on no assumptions on how the size andcomposition of the central bank’s balance sheet will respond after the bank makes apolicy decision. We identify two types of policy shocks as “anticipated” shocks thatbest portend the current and future paths of these policy instruments in response tothem. We obtain evidence that qualitative easing shocks have expansionary effects on
the economy while quantitative easing shocks do not.
JEL Classification: E52, E58.
Keywords: quantitative easing; qualitative easing; conventional monetary policy; vector autoregressive model; anticipated shock* This paper was previously circulated under the title “Identifying Unconventional Monetary Policy
Shocks” but has since been substantially revised. We are grateful for the helpful comments and discussions by
Kosuke Aoki, Natsuki Arai, Yoosoon Chang, Michael Dowell, Hiroshi Fujiki, James Hamilton, Naoko Hara,
Philipp Hartmann, Yuzo Honda, Takeo Hoshi, Hibiki Ichiue, Keiichiro Inaba, Ryo Jinnai, Atushi Kataoka, Ryo
Kato, Junko Koeda, Kenneth Kuttner, Ko Munakata, Michael Murach, Ryuzo Miyao, Yoji Morita, Yasutomo
Murasawa, Tatsuyoshi Okimoto, Hugh Patrick, Mototsugu Shintani, Etsuro Shioji, Shigenori Shiratsuka,
Koichiro Suzuki, Takeru Terao, Yoshiro Tsutsui; by seminar participants from the Bank of Japan, Columbia
University, Hitotsubashi University, Kobe University, and Konan University; and by conference participantsfrom the 2016 Japanese Economic Association Autumn Meeting, 2017 Western Economic Association Annual
Meeting, 2018 China Meeting of the Econometric Society, 2018 Asian Meeting of the Econometric Society,2018 International Association for Applied Econometrics Annual Conference, and 2019 CEBRA Annual
Meeting. We would also like to thank Miho Kohsaka for providing excellent research assistance. This study
received financial support in the form of Grants-in-Aid from the Japanese Ministry of Education and Science.
The views expressed in this paper are those of the authors and do not necessarily reflect the official views of
the Bank for International Settlements.
†
Kiyotaka Nakashima, Independent Economist, e-mail: nakakiyo1027@gmail.com‡
Masahiko Shibamoto, Research Institute for Economics and Business Administration, Kobe University,e-mail: shibamoto@rieb.kobe-u.ac.jp§
Correspondence to: Koji Takahashi, Bank for International Settlements, e-mail: koji.takahashi@bis.org

1

Introduction

In this paper, we introduce a novel identification approach to disentangle the causal effectsof the Bank of Japan (BOJ)’s quantitative and qualitative monetary policy shocks in its
balance sheet operations. More specifically, our new strategy addresses two issues entailed
in identifying the unconventional monetary policy shocks in the vector autoregressive(VAR) analysis: the endogeneity of the monetary policy indicators, and quantitative andqualitative monetary policy shocks as “anticipated” shocks. By identifying quantitativeand qualitative monetary policy shocks, we provide robust evidence that the quantitativeeasing shock, the shock that increases the size of the BOJ’s balance sheet, significantly
decreases the long-term nominal interest rate without conferring any favorable effects onreal economic activity. Specifically, the impulse response analysis shows that a quantitative
shock that increases the monetary base by 10 percentage points, in the long run, has slightly
negative effects on the output gap, inflation rates, and stock prices although it significantly
decreases 10-year government bond yield as well as short-term rates. On the other hand,the qualitative easing shock, the shock that increases the BOJ’s unconventional asset ratio
to its total assets, brings about expansionary effects. We find that a qualitative easing
shock of increasing the unconventional asset ratio by 0.3 percentage points stimulates theoutput gap by 0.1 percentage points and the inflation rate by 0.4 percentage points while
significantly increasing stock prices. Also, we show some suggestive evidence that thequantitative easing shocks cause a negative effect because it has a signaling effect regarding
the future path of the economy. Finally, we find that the conventional easing monetary
policy shock, the increase in the short-term policy rate, has an expansionary effect even in
the low-interest rate period.
Central banks have several monetary policy options, even with the policy rate at aneffective lower bound (Bernanke and Reinhart (2004)). For example, in March 2001, theBOJ adopted a quantitative easing policy by setting the targeted overnight call rate toalmost 0%. Under this policy framework, the monetary base, or size of the BOJ’s balance
sheet, expanded through the growth of excess reserves in the BOJ’s current account bases(see Figure 1). The BOJ discontinued its quantitative easing policy in March 2006 but

2has kept the targeted rate well below 0.5% since then. In its quantitative and qualitativeeasing policy introduced in April 2013, the BOJ further deepened its unconventional
policy framework not simply by enlarging its balance sheet, but by increasing the ratioof unconventional assets, such as long-term JGBs and risk assets (e.g., exchange-tradedfunds (ETF) and real estate investment trusts (REIT)), on its balance sheet.1 Central banks
in advanced economies such as the U.K., U.S., and Euro area countries have followedwith their own unconventional policy frameworks characterized by similar increases inthe sizes of the central bank balance sheets and changes in the balance sheet compositions
at extremely low policy-targeted interest rates.
While the actual implementation of the unconventional monetary policy in many countries has stimulated empirical research on unconventional policy effects using the structural
VAR model, the policy effects on the real economy are still disputable.2 One of the biggest
challenges in assessing unconventional policy effects by VAR analysis is the choice of variables to use as monetary policy indicators that precisely reflect the central bank’s policy
decisions in the unconventional monetary policy. Starting from the premise that monetary aggregates such as the monetary base and excess reserves represent a central bank’s
policy stance, several previous studies have used reduced-form VAR innovations of thosevariables as exogenous components of the unconventional monetary policy (Iwata andWu (2006), Inoue and Okimoto (2008), Honda et al. (2013), Kimura and Nakajima (2016),
Miyao and Okimoto (2017), and Hayashi and Koeda(2019)).3 This empirical strategy is essentially an extension of the standard recursive VAR approach to estimate the effects of theconventional monetary policy of controlling short-term nominal interest rates (Bernanke
and Blinder (1992) and Christiano et al. (1996)).4
Other empirical studies on unconventional policy effects have employed a strategy that
does not require for one-to-one mapping between an observable monetary policy indicator
1

See Shiratsuka (2010) and Ueda (2012) for a detailed explanation of unconventional assets in Japan.
See Ugai (2007) and Joyce et al. (2012) for a survey of the empirical research on unconventional policy
effects.
3
Previous studies applying the recursive VAR approach to unconventional monetary policy in the U.K.
and U.S. have not necessarily used the monetary base or excess reserves as an unconventional monetary
policy indicator. Wu and Xia (2016), for example, used shadow policy rates for an analysis of the U.S., andWeale and Wieladek (2016) used asset purchase announcements for analyses of the U.K. and U.S.
4
Rudebusch (1998) and Nakamura and Steinsson (2018) discuss concerns underlying the use of thestandard recursive VAR approach to identify monetary policy shocks.
2

3

and a monetary policy shock. By assuming that unconventional monetary policy shocks
can be represented collectively as a single unobservable shock, they apply a VAR analysis
that imposes sign restrictions on the impulse responses of the macroeconomic variables
to single monetary policy shocks (Kapetanios et al. (2012), Baumeister and Benati (2013),
Schenkelberg and Watzka (2013), Gambacorta et al. (2014), and Weale and Wieladek(2016))5 and heterogeneous variance restrictions on the intensity of structural shocks,
including single policy shocks (Wright (2012), Rogers et al. (2014), and Shibamoto andTachibana (2017)).6
However, since central banks utilize different policy tools in the low interest rate environment, the two aforesaid empirical strategies are insufficient to assess the effects ofunconventional policy. In the case of Japan, the BOJ has purchased a vast range of different
financial assets such as exchange trade funds, commercial papers, and long-term government bonds. To address this issue, we assume that the unconventional monetary policy
implemented by the BOJ in its balance sheet operations has two aspects: a quantitativeand qualitative easing.7 In this paper, we propose a method for identifying the BOJ’squantitative and qualitative monetary policy shocks in VAR analysis.
Another identifying issue is how monetary policy indicators respond to policy changes.
As discussed above, previous studies on unconventional policy effects based on VAR analysis have taken either of two approaches. Some have regarded reduced-form VAR innovations of monetary aggregates such as the monetary base as unconventional policy shocks.
Others have imposed restrictions on the impulse responses of some of the variables to a single unobserved unconventional policy shock. Regardless of the difference in methodology,
both of these approaches assume that all monetary policy shocks to monetary aggregates5

A naive sign restriction would fail to extract any information from data. See Baumeister and Hamilton(2015).
6
Non-VAR approaches that assume a single unobservable unconventional monetary policy shock also
include the event study approach (Gagnon et al. (2011), Krishnamurthy and Vissing-Jorgensen (2011), Joyce
et al. (2011), Swanson (2011), and Ueda (2012)) and the difference-in-difference approach (Foley-Fisher et al.
(2016) and Rodnyansky and Darmouni (2017)).
7
As we discuss in Section 2.2, we use the BOJ’s unconventional asset ratio to capture the qualitative easing
shock and we include various financial assets from long-term government bonds to stocks as unconventional
assets to calculate the ratio. However, purchasing different unconventional assets could have different effects
on the economy. In this paper, we do not investigate each of the different effects because we focus on theentire effect of purchasing unconventional assets.

4

are “unanticipated,” and both approaches provide evidence that unconventional policy
shocks yield favorable effects on the macroeconomy. The identification of shocks, however,is unsuitable in terms of the actual dynamics of the unconventional and conventional policy indicators. More specifically, the size and composition of a central bank’s balance sheet
may not reflect the policy changes of the central bank immediately after an announcement,
whereas the bank’s policy rate does. As the BOJ clarifies in its statement, the target levelsof unconventional policy instruments are basically achieved after several months or a year
has passed from the BOJ’s policy change announcement. Hence, agents in the economy
can anticipate large changes in monetary policy indicators, including the monetary base,even in the long-run future. If, however, we impose an existing identification scheme in a
VAR model such as a recursive restriction and a sign restriction and ignore the difference
between those unconventional policy indicators and the short-term policy rate, we run therisk of misspecifying those anticipated changes as unanticipated shocks.
Premising that monetary policy shocks are mainly attributable to the actual movementsof observable unconventional policy indicators, we identify two unconventional monetary
policy shocks relating to the size and composition of the BOJ’s balance sheet as anticipatedshocks, or news shocks, that best presage their current and future paths.8 We identify theunconventional shocks using the maximum forecast error variance (MFEV) approach from
Francis et al. (2014), a method that builds on the work of Faust (1998) in the framework ofmonetary policy analysis. The MFEV approach identifies a shock such that its contribution
to the forecast error variance of a time series process is maximized over all horizons up
to a finite truncation horizon, whereas Faust’s approach maximizes the contribution at
a predetermined finite horizon. The effective use of the MFEV approach to identify theunconventional shocks in the BOJ’s balance sheet operations requires a long truncation
horizon, because the monetary base and composition of assets in the BOJ’s balance sheetschange only gradually after the BOJ announced the policy change as we discuss in Section
2.2. in more details. Therefore, when employing the MFEV approach, we adopt the8

Milani and Treadwell (2012) tried to theoretically disentangle the anticipated and unanticipated components of policy shocks by constructing a New Keynesian model that incorporates news about future
policy rates. Tsuruga and Wake (2019) find that a time lag between the decision and implementation ofmoney-financed fiscal stimulus may cause a recession by using New Keynesian DSGE model, indicating theimportance of distinguishing between anticipated and unanticipated stimulus.

536-months truncation horizon.9
Two features of the MFEV approach make it more agnostic and flexible than the existing approaches in identifying unconventional policy shock. First, the MFEV approachrequires no assumptions on signs of responses of the central bank’s two balance sheet instruments. Second, the approach isolates the primary driver of a time series process as an
anticipated shock and can be applied to any case in which the same dominant driving process exists (Francis et al. (2014)). The MFEV approach is suitable for identifying the two
unconventional monetary policy shocks, given that the BOJ implements the unconventional monetary policy by altering the expected future course of monetary policy actions,
including the balance sheet operations in its statement (Okina and Shiratsuka (2004)).
Finally, the endogeneity issue is one of main difficulties for the identification of monetary policy shocks. We should note that a simple MFEV relies on the variance-covariancematrix of VAR residuals. However, using simple VAR residuals would suffer from theendogeneity problem. To overcome this problem, we follow the literature of the external
instrument variable approach for a structural VAR (SVAR) model and combine it with the
MFEV method. To our knowledge, this is the first paper to combine the MFEV approachwith the external instrument variable method for the identification of a SVAR model. Moreprecisely, following in the vein of the previous literature, we focus on the monetary policy
meeting days as the timing when monetary policy shocks arise in the economy (Kuttner(2001), Cochrane and Piazzesi (2002), Gürkaynak et al. (2005a; 2005b; 2007), Honda andKuroki (2006), Campbell et al. (2012), Gertler and Karadi (2015) and Nakamura and Steinsson (2018)). The BOJ decides its policy scheme at monetary policy meetings (previously,
the meetings were held once or twice a month) and publicly states its policy decision justafter each meeting. We exploit the idea that monetary policy shocks are reflected in thechanges of asset prices just after the BOJ deploys its main communication tool, the public9

Weale and Wieladek (2016) and Zeev et al. (2019) share the similar motivation with us in identifying
their unconventional monetary policy shocks. In addition to the recursive restriction and the sign restriction
approach, Weale and Wieladek (2016) also employed Faust’s (1998) approach to analyze the U.K. and U.S.
unconventional monetary policy. They identified the asset purchase announcement shock as the processthat most robustly explained the forecast error variance of asset purchases, with a three-month delay. On
the other hand, like us, Zeev et al. (2019) employed the MFEV approach with a much longer truncation
horizon. They identified the US forward guidance shock regarding the future path of the short-term policyrate, with the 15-, 30-, or 45-months truncation horizon.

6statement that it issues on the latest monetary policy meeting. In other words, we use
the market responses to the BOJ’s policy decision statements, that is, the monetary policy
surprises in financial markets or the revised expectations of market participants embedded
in financial asset valuations.10 Such price revisions in the financial markets can be used
to measure the extent to which monetary policy announcements surprise the markets.
We thoroughly utilize this insight to identify the BOJ’s monetary policy shocks. Moreconcretely, as long as we correctly characterize the monetary policy surprises, we can use
them as the instrumental variables of the reduced-form VAR innovations to identify thecausal effects of the BOJ’s monetary policy shocks on macroeconomic variables.11
The remainder of this paper is organized as follows. Section 2 constructs our monetary
policy surprise measures and examines the movement of each policy indicator in responseto the BOJ’s monetary policy shocks. Section 3 discusses a method to identify quantitative and qualitative monetary policy shocks as anticipated shocks. Section 4 reports theestimation results for the unconventional monetary policy shocks. Section 5 explores therobustness of our empirical findings on unconventional monetary policy effects, alongwith several implications of the findings. Section 6 closes the paper with concluding comments. The Appendix provides detailed definitions of the variables used in this paper anddetailed discussions on the development of the monetary policy indicators and estimatedmonetary policy shocks.
10

Through this paper, we use the term “agents’ revisions of expectations” to refer to the monetary policy
surprises, or the revised expectations of market participants embedded in financial asset valuations asfactors correlated with monetary policy shocks, whereas some studies measured such expectation revisions
either directly through survey forecasts or indirectly through models that are designed to approximate
the expectation formation process (see e.g., Coibion and Gorodnichenko (2012)). We acknowledge ananonymous referee for suggesting this point.
11
See Ramey (2016) and Stock and Watson (2012; 2018) for detailed surveys of this empirical strategy for
identifying U.S. monetary policy shocks using monetary policy surprises, namely, changes in asset market
prices on Federal Fund Open Market Committee dates.

7

2

Monetary Policy Surprises and the Movements of Monetary Policy Indicators in Response

As we discussed earlier in the Introduction, the fundamental issue to consider in identifying monetary policy shocks in relation to policy indicators is the timing of the central bank’s
policy decision announcement. After beginning this section with a discussion of the sourcefrom which monetary policy shocks originate, we examine the movements of monetary
policy indicators in response to monetary policy shocks. In doing so, we demonstrate why
it becomes necessary to apply our method of using the structural VAR approach to identify
monetary policy shocks in relation to each of three policy indicators, one conventional andtwo unconventional. The conventional policy indicator is the uncollateralized overnight
call rate, that is, the BOJ’s targeted short-term policy rate. The unconventional policy indicators are the monetary base and the composition ratio of the BOJ’s unconventional assets
to its total assets. The unconventional assets include long-term JGBs, ETFs, stock, REITs,
commercial papers, and corporate bonds. In this paper, we categorize all risky assets into
one category as unconventional assets and use the risky asset ratio as a policy instrument
variable. This is because the appropriate number of factors required for the explanation isestimated to be three. Therefore, increasing the number of instruments to more than three
generates complexity but does not provide better understanding of the role of each policy
tool. In addition, the BOJ categorizes its unconventional policies into two dimensions.
For example, Governor Kuroda12 mentioned that the different purchasing programs have
two aspects, namely quantity and quality easing.13 Within the framework of the BOJ’s unconventional monetary policy, the unconventional assets ratio is regarded as a qualitative
policy indicator (Shiratsuka (2010) and Ueda (2012)), while the monetary base, or the sizeof the BOJ’s balance sheet, is regarded as a quantitative policy indicator.
12

More precisely, he mentioned as follows, “that is, the central banks’ purchases of government bonds
and other assets from the markets have the effect of encouraging further declines in long-term interest rates
and lowering risk premia of asset prices by absorbing risks. . . Thus, it is important to work on two aspectsof monetary easing, both in terms of quantity and quality.” in the speech at the meeting held by the Yomiuri
International Economic Society in Tokyo (April 12, 2013)13
However, the detailed categorization and separate examination of each asset purchasing program could
help us to understand the mechanism and effect more precisely. This will be our future research topic. We
thank an anonymous referee for pointing out this important issue.

8Note that we use the sample period from April 1998 to January 2016 throughout thispaper. We selected this sample period for two reasons: first, because the BOJ publishes
detailed data on its asset composition from April 1998; second, because the transmission
mechanism through control of the short-term rate may change when the policy rate turns
negative after the BOJ introduces a negative interest policy in February 2016 (see e.g.,
Eggertsson et al. (2019) and Abadi et al. (2022)). The Appendix provides detailed
definitions of the variables used in this paper.

2.1

Monetary Policy Surprises

The BOJ decides its policy scheme in a monetary policy meeting (MPM) held about twice
per month and publicly states its policy decisions just after the meeting closes. We canassume, therefore, that the BOJ’s monetary policy shocks are reflected in revisions in the
expectations of agents in the asset markets. This empirical strategy helps us overcome
identification problems that would arise with regard to endogenous responses of monetary
policy if we simply treated innovations of monetary policy indicators as policy shocks in amonthly or quarterly VAR model. If we were to apply the innovations in such VAR models,the models would be contaminated by their endogenous responses to the underlying
financial variables and other macroeconomic variables left out of the VAR system (Romerand Romer (2004), Faust et al. (2004), Gertler and Karadi (2015), and Shibamoto (2016)).14
As an alternative, therefore, we use monetary policy surprises in asset markets, or revisions
in the expectations of agents in asset markets, as external instruments to control for theendogenous responses of the three monetary policy indicators not only to the financial
variables in the VAR, but also to underlying correlated variables out of the VAR. Thisapproach will be discussed in the next section.
Previous studies constructed monetary policy surprises by focusing on changes inshort-term interest rate futures and using high-frequency daily trading data. Kuttner(2001), Cochrane and Piazzesi (2002), Gürkaynak et al. (2005a; 2005b; 2007), Campbell
14

Romer and Romer (2004), Faust et al. (2004), Gertler and Karadi (2015), and Shibamoto (2016) pointed
out that the reduced-form VAR innovations of policy rates would have a substantial bias in identifying themonetary policy effect.

9et al. (2012), Gertler and Karadi (2015) and Nakamura and Steinsson (2018) constructedmonetary policy surprises in federal funds or Eurodollar futures occurring on Federal
Open Market Committee dates. Honda and Kuroki (2006) constructed monetary policy
surprises in euro-yen futures occurring on the BOJ’s MPM dates from 1989 to 2001. Although these studies examined financial market responses to exogenous monetary policy
shocks under the conventional policy regime, this empirical strategy is still useful for
identifying the BOJ’s monetary policy shocks under the unconventional policy regime.
This strategy, however, is of limited use for our purposes, given that short-term interest
rate futures have hardly changed since the BOJ introduced its unconventional monetary
policy. Here, therefore, we depart from the previous studies by looking beyond changes ina particular asset market and exploiting all information on changes in the major financial
markets just before and just after the BOJ’s public statements. More concretely, we employ
the principal component approach of Bernanke et al. (2004), Gürkaynak et al. (2005b),
and Swanson (2017) to prepare for monetary policy surprises as common factors of unanticipated changes in the major financial market variables following public statements. If
we obtain l common factors for market participant surprises over a central bank’s policy
decisions, we can construct at most l types of monetary policy shocks.
The principal component analysis of monetary policy on meeting day t is based on thefollowing equation:
Xt = ΛFt + ηt ,(1)

where Xt = (x1t , .., xnt )′ denotes the vector of n financial time series, ηt indicates the vector ofn idiosyncratic disturbance terms, Ft is the vector of l unobserved common factors, and Λ isa matrix of the coefficients identified as factor loadings. We aim to extract common factors
Ft by using the factor model. We include 12 financial market variables xit (i = 1, .., 12): one

futures rate (three-month euro-yen TIBOR futures), five yen interest swap rates (1, 2, 5,
10, 30 years), one short-term spot rate (three-month euro-yen TIBOR), two spot exchange
rates on the Tokyo market (yen-U.S. dollar and yen-AUS dollar), two stock indexes (TOPIX

10and Nikkei JASDAQ), and bank reserve deposits.15
Our inclusion of asset variables in calculating principal components is similar to that in
Swanson (2021); that is, he exhaustively utilized the information on various types of asset
prices, such as federal fund futures, Euro-Dollar futures, Treasury bond yields. In addition,
we include the stock market index and exchange rates in order to capture the BOJ’s policy
measures appropriately. More specifically, the BOJ started to purchase exchange-tradedfunds that track stock indexes from 2010 and then expanded the purchasing amount. In
2021, it reaches about 5 percent of the total market value of all stocks listed in the firstsection of the Tokyo Stock Exchange. In addition, the BOJ has paid special attention to
exchange rates, as Governor Kuroda pointed out in his speech.16 To capture the distinct
feature of the BOJ’s monetary policy, we include the stock market index and exchange
rates. This approach allows us to investigate what number of dimensions best describes
monetary policy without selecting one particular asset price as a sufficient statistic formonetary policy, as in Gertler and Karadi (2015) and Nakamura and Steinsson (2018), who
aimed at summarizing monetary policy with the 1- or 2-year Treasury yield.”
We calculate the differences in the seven interest rate variables and the log differencesof the two exchange rates, two stock indexes, and bank reserves as percentages of the rateof change before and after public statements. More concretely, we use the closing values
at 3:00 p.m. from the day before the public statement to the day after the statement tocalculate changes of the 12 financial variables over the two-day period in order to duly
consider the timing of the public statement and the time required for the news to be
sufficiently recognized (see Ueda (2012)).17 That is, for stock prices, exchange rates, and15

In the baseline specification, we do not control for macroeconomic news about real economic activity
or inflation in the dynamic factor model. Hence, our monetary policy surprises could include informationon the macroeconomic news other than the monetary policy itself. Following an anonymous referee’s
suggestion, to control for macro news release on policy meeting days, we use the macroeconomic news
shocks defined as the difference between an actual value (for the index of industrial production and theconsumer price index) and its market forecasts from the Monetary Market Services (MMS) survey. We foundthat our results reported below do not change much qualitatively and quantitatively. We acknowledge thereferee’s suggestion.
16
Governor Kuroda pointed out that one of the four major channels of monetary easing is “a channel
through which the yen depreciate due mainly to an expansion in yield differentials between Japan and othereconomies in the speech at the Meeting of Councillors of Nippon Keidanren (Japan Business Federation) on
December 23, 2021.
17
An event study analysis by Ueda (2012) showed that asset prices, including TOPIX and Japanese
government bond yields, significantly respond to monetary policy changes from two days after the BOJ’s

11bank reserves, xit is defined as follows:
xit = log(Pit+1 /Pit−1 ) × 100,(2)xit = rit+1 − rit−1 ,(3)and for interest rates,

where Pit+1 and Pit−1 indicate the closing values of exchange rates, stock indexes, and bankreserves on the day after a monetary policy meeting and the closing values of the sameon the day before the monetary policy meeting, respectively, and rit+1 and rit−1 denote theclosing interest rates.
We preliminarily exclude the dates of the meetings at which the BOJ coordinated policy
with the Fed, the European Central Bank, or the Bank of England. We also exclude thedate on which the BOJ agreed on its policy responses to the Tohoku earthquake of March
11, 2011, as policy coordination and disaster response would be likely to contaminate the
BOJ’s policy effects.18
We select the number of common factors using the information criteria proposed by
Bai and Ng (2002) and Ahn and Horenstein (2013): the former and the latter respectively
suggested that the preferred model is the one that minimizes and maximizes the information criteria. Table 1 reports the two information criteria applied. These criteria suggestthat we should adopt three common factors as monetary policy surprises in the twelve
financial markets.19 We can therefore construct at most three types of monetary policy
shocks.
When constructing monthly data on the monetary policy surprises, we extract the
public statements onward. For a robustness check, however, we also used narrower time windows to extract
the monetary policy surprises. We found that the results are qualitatively the same as those reported below.
18
The BOJ held meetings on September 18, 2008, September 29, 2008, and November 30, 2011 to coordinate
policy. In a meeting on March 14, 2011, the BOJ agreed on its policy response to the Tohoku earthquake.
19
To examine the importance of including the stock market indexes and exchange rates, we also conduct
factor analysis by excluding them. If we exclude those variables, we would summarize monetary policyby just one dimension. However, this is implausible when we consider the fact that the BOJ implemented
various unconventional policy measures in this period, as discussed in Swanson (2021) for the Fed’s policy.
In other words, we would miss other substantial dimensions of monetary policy that involve the stock priceindexes and exchange rates if we exclude them. We thank the anonymous referee for pointing out thisimportant issue.

12common factors jointly over all the surprises and then use a sum within each month, as in
Romer and Romer (2004) and Barakchian and Crowe (2013).

2.2

Monetary Policy Indicators’ Response

In this subsection we examine the statistical relevance among the monetary policy surprises
and monetary policy indicators based on how differently each of the monetary policy
indicators responds to monetary policy shocks whose information is contained in monetary
policy surprises. To this end, we run the following distributed lag regression of the policy
indicators on the current and lagged monetary policy surprises:20
PIt =

3 X
H
X

j

j

rh PSt−h + Controls + ePI
t ,

(4)

j=1 h=0

where PIt denotes the change or the level in each of the monetary policy indicators—theshort-term policy interest rate (SR), monetary base (MB), and composition ratio of the BOJ’s
unconventional assets to total assets (COMP) —in month t. The change in the monetarybase is expressed using the monthly growth rates (annual rate) of the log-differenced
jvalues. The level in the monetary base is expressed using logarithmic values × 1200. PSt−hdenotes the h lagged values for the three monetary policy surprises generated using thefactor analysis. ePIt denotes stochastic disturbances. Controls include a constant term andthe one-lagged value of PIt .
j

Table 2 reports Chi-square statistics and P-values for testing the null hypothesis, rh = 0
for all j = 1, 2, 3, in the distributed lag regression at the horizon of h = H. As the tableshows, the monetary policy surprises are statistically correlated with the monetary policy
indicators but associate with the indicators in different ways. Specifically, we find that themonetary policy surprises are significantly associated with the short-term policy rate (∆SRt
and SRt ) at the horizon of H = 0, or the contemporaneous time of the policy shock arrival.

This association tells us that the short-term policy rate immediately responds to the BOJ’s
policy changes. In contrast, monetary policy surprises show no significant associations
20

This regression is essentially the same as the local projection method (see Jordà (2005) and Stock andWatson (2018)).

13

with the monetary base (∆MBt and MBt ) or the unconventional assets ratio (∆COMPt and
COMPt ) at the horizon H = 0, but are significantly associated with the monetary baseat H ≥ 12 and with the unconventional assets ratio at H ≥ 2. These estimation results
imply that the monetary base and unconventional assets ratio respond to the BOJ’s policychanges slowly and later in time.
Our finding on the responses of the quantitative and qualitative monetary policy indicators clearly indicates that monetary policy surprises have substantial information on
their future movements, but not on their contemporaneous ones. In other words, thepublic statements issued just after the MPM on the bank’s decision to change the two unconventional policy indicators behave like anticipated shocks that portend future changes
in the indicators. Therefore, if we were to impose an existing identification scheme in a
VAR model such as a recursive restriction and a sign restriction and ignore the difference
between those unconventional policy indicators and the short-term policy rate, we would
misspecify those anticipated changes as unanticipated shocks. In the next section we incorporate these medium- and long-term findings among the monetary policy surprises andtwo unconventional policy indicators into an identifying restriction on the intertemporal
relations among the unconventional monetary policy shocks and indicators.
Note, also, that each of the monetary policy indicators associates differently with themonetary policy surprises. The differences between the associations compel us to separately identify the three monetary policy shocks relating to the three policy indicators: one
conventional monetary policy shock that aims to exogenously change short-term nominalinterest rates and two unconventional monetary policy shocks that aim to exogenously
change the size and composition of the central bank’s balance sheet.

3

Identifying Quantitative and Qualitative Monetary Policy Shocks

This section describes the empirical strategy we use to identify the effects of the two
unconventional monetary policy shocks (quantitative and qualitative monetary policy

14

shocks) and the one short-term policy rate shock with the three principal componentsof the monetary policy surprises in the structural VAR analysis. First, we assume thatmonetary policy shocks originate from the public statements released just after the MPM.
Second, we account for the identifying restrictions that incorporate the features of themonetary policy indicators discussed in Section 2. Specifically, we impose restrictions on
unconventional monetary policy shocks as shocks that capture current and future changes
in the size and composition of the BOJ’s balance sheet, and we define the short-term policy
rate shock as a shock that is extracted after the unconventional monetary policy shocks.
In Section 5 we show that estimated impulse responses to the quantitative and qualitative
policy shocks based on this assumption are robust even under the alternative assumptionthat the policy rate shock is followed by the two unconventional policy shocks involving
the central bank’s balance sheet operations.
Also note that in setting the VAR model, we change our assumptions about the entry ofa new policy scheme in an unconventional monetary policy regime. When, for example,
the central bank introduces or halts a zero interest rate policy, quantitative easing policy,
or quantitative and qualitative easing policy, we assume that the new scheme reflects nota change in the central bank’s deep parameter in its policy decision announcement, but apolicy shock that either portends future changes in the monetary base and unconventional
assets ratio or leads to an immediate change in the short-term policy rate. Below, therefore,
we make no use of regime-switching and time-varying parameter VAR models such as
those of Fujiwara (2006), Kapetanios et al. (2012), Baumeister and Benati (2013), Kimura
and Nakajima (2016), Miyao and Okimoto (2017), Hayashi and Koeda (2019), and Koeda(2019).
Our procedure for VAR identification is based on the following two-step approach. In
the first step, we use the monetary policy surprises as the instrumental variables of the
reduced-form VAR innovations of the three policy indicators and other macroeconomic
variables. Specifically, we construct an impact matrix for the instantaneous responses of
the VAR variables by disentangling the causal relationships among the monetary policy
shocks and VAR variables. The impact matrix in this stage disregards the movementin the unconventional policy indicators following policy changes. We therefore impose
15

restrictions, in the second step, to identify the quantitative and qualitative shocks, which
we define as shocks that best explain the changes in the conditional expectation about thecurrent and future paths of the size and composition of the central bank’s balance sheet. To
this end, as discussed in the Introduction, we employ the maximum forecast error variance(MFEV) approach from Francis et al. (2014).

3.1

Structural VAR Model

Letting yt denote a K × 1 vector of time-varying observables in month t, this stochastic
structure can be expressed in terms of the vector moving average representation:yt = Φ(L)ut ,(5)

where Φ(L) = I + Φ1 L + Φ2 L2 + · · · is a matrix polynomial in the lag operator, L, and ut
denotes the K × 1 vector of the reduced-form VAR innovations. The monetary base (MB),unconventional assets ratio (COMP), and short-term policy rate (SR) are given by the first,
second, and third elements of yt , respectively. The structural vector moving average
representation can thus be written as follows:
yt = Ψ(L)ϵt ,(6)where Ψ(L) = Ψ0 + Ψ1 L + Ψ2 L2 + · · · , and ϵt denotes the K × 1 vector of the structural
QL SR
QN
QL
shocks. Let ϵMP
be the 3 × 1 policy shock vector ϵMP= [ϵQNttt , ϵt , ϵt ] , where ϵt , ϵt ,′and ϵSRt denote unconventional quantitative, qualitative policy shocks, and conventional
short-term interest rate shocks, respectively. The space spanned by the policy shock vector
ϵMP
is disentangled from the space spanned by other possible shocks of the (K − 3) × 1
tvector ϵXt in the following linear relation between the reduced-form VAR innovations ut
and structural shocks ϵt :ut = Rϵt = RMP ϵMP+ RX ϵXt ,
t
′

X
] ,
R =[RMP , RX ] , ϵt =[ϵMPt , ϵt(K × K)(K × 3) (K × (K − 3))(K × 1)(3 × 1) ((K − 3) × 1)

16

(7)

where RMP represents the impact matrix for the responses of the VAR variables yt to themonetary policy shocks.
The variance-covariance matrix of the space spanned by the monetary policy shocks
can be expressed as,
′

′

′

MP
ΣMP = RMP E(ϵMP)RMP = RMP RMP ,
t ϵt

(8)

where the variance of monetary policy shocks is normalized to one. The impact matrix
RMP satisfies the variance-covariance matrix but it is not unique. For some arbitrary

orthogonalization of this impact matrix, R̃MP , the entire space of possible impact matrices
can be written as:
RMP = R̃MP D,(9)where D denotes the 3 × 3 orthonormal matrix (DD′ = I). Note that R̃MP d j ( j = 1, 2, 3) (whered j is the 3 × 1 orthonormal vector indicating the jth column of the orthonormal matrix D)
is the K × 1 vector, and thus interprets the contemporaneous impact of the jth monetary
policy shock on the VAR variables. In the following subsections we construct this impact
matrix (9) to identify the three types of monetary policy shocks.

3.2

Controlling the Endogeneity of the Monetary Policy Indicators

We use three principal components of the monetary policy surprises (PSt ) extracted fromthe changes in the twelve major financial markets on MPM days as instrumental variables
for the reduced-form VAR innovations, ut . Thus, we aim to control for the endogeneity ofthe monetary policy indicators and disentangle the causal effects of the policy shocks on
the VAR variables at the shock arrival time. More concretely, we conduct the following
system regression:
ut =RPS PSt +et ,(K × 3)(3 × 1)

17

(10)

where PSt denotes the 3 × 1 vector of the three monetary policy surprises at a monthly
frequency. The system regression yields the instantaneous responses of the VAR variables
psto the BOJ’s public statements in the form of fitted values ut = R̂PS PSt . We then obtainthe following variance-covariance matrix incorporating the instantaneous impacts of the
public statements on the VAR variables:
ps ps′

ΣPS = E(ut ut ).

(11)

A diagonal element of this variance-covariance matrix, ΣPS(i = 1, · · · , k), includes thei,iinstantaneous forecast error variances of the VAR variables attributable to the BOJ’s publicstatements on MPM days.21

3.3

Identifying Quantitative and Qualitative Monetary Policy Shocks

Here we describe the second-step procedure to identify the conventional and unconventional monetary policy shocks. We identify the unconventional monetary policy shocks
with help from the monetary base and unconventional assets ratio, assuming that agentsbelieve that the policy indicators will meet their target levels after the BOJ’s public statements on MPM days. To incorporate this feature into our identification of the unconventional monetary policy shocks, we define them as anticipated shocks that best portend thecurrent and future paths of the monetary base and unconventional assets ratio.
This identification strategy requires that we model changes in the expectations regarding the current and future paths of the unconventional policy indicators. We do so by
employing the MFEV approach proposed by Francis et al. (2014).22 In this approach, we
specify the changes in the conditional expectation based on a VAR model as maximization
problems for the contributions of the unconventional policy shocks to the h-step-ahead
forecast error variances of the unconventional policy indicators.
21

We find that the VAR innovations of the monetary policy indicators differently load on a linear combination of the three principal components through system regression (10) as shown in Table 3. For moredetails, see Section 4.1.
22
Barsky and Sims (2011) employed the MFEV approach to identify anticipated shocks related to futuretechnology. Zeev et al. (2019) used this approach to identify anticipated monetary policy shocks in the U.S.
conventional monetary policy regime.

18

To explain the MFEV approach, we begin by expressing the h-step-ahead forecast error
conditioning on the structural shocks ϵt :yt+h − Et−1 yt+h =hXΦτ Rϵt+h−τ =hXΦτ RMP ϵMP
t+h−τ +

h
X

(12)

τ=0

τ=0

τ=0

Φτ RX ϵXt+h−τ ,where the first and second equalities use equations (5) and (7). The h-step-ahead forecast
error due to monetary policy shocks ϵMPt+τ can therefore be expressed as:hX
τ=0Φτ RMP ϵMP
t+h−τ =hXΦτ R̃MP DϵMP
t+h−τ ,

(13)

τ=0where the equality uses equation (9). If we have orthogonalization matrix R̃MP and orthonormal vector d j (j = 1, 2, 3), we can therefore generate the impulse responses of theVAR variables to the jth monetary policy shock from the impact vector R̃MP d j .
We first prepare for an orthogonalization matrix R̃MP such that it satisfies the following
condition:′′

ΣPS = R̃MP DD R̃MP .

(14)This equality ensures that the impact matrix R̃MP D is determined based on the estimatedinstantaneous responses R̂PS of the reduced-form VAR innovations to the central bank’s
public statements in equations (10) and (11). Next, we employ the MFEV approach, thereby
obtaining the orthonormal vector d j (j = 1, 2, 3) with a given orthogonalization matrix R̃MP
satisfying equation (14), as discussed below.
From the h-step-ahead forecast error (13), the share of the h-step-ahead forecast error
variance (FEV) of the unconventional policy indicator i (i = 1, 2) attributable to the associated unconventional monetary shock ϵMP( j = i) is expressed as a variance decompositionj,t

19

of the following form:
FEV of policy indicator i due to ϵMP
j,t

z
h
X
′

Ωij (h) =

ι1i

Ph

τ=0 Φτ R̃

′

′

MP′

′



Dι2 j ι2j D R̃ Φτ ι1i


=
P
′
′
ι1i hτ=0 Φτ ΣPS Φτ ι1i
MP

}|

{
′

′

′

Φi,τ R̃MP d j d j R̃MP Φi,τ

τ=0
h
X

,

(15)

′

Φi,τ ΣPS Φi,τ

τ=0

|

{z

}

FEV of indicator i due to public statements

where i (i = 1, 2) indicates the place of the unconventional monetary policy indicators(MB and COMP) in vector variable yt , and j ( j = i) indicates the place of the associated
QL
SRMP
unconventional policy shocks ϵQNt , ϵt , and ϵt in policy shock vector ϵt . ι1i and ι2j are theK×1 and 3×1 selection vectors, with ones in the ith place and jth place and zeros elsewhere,
and d j is the 3 × 1 orthonormal vector indicating the jth column of the orthonormal matrix
D. The selection vectors outside of the parentheses in both the numerator and denominatorι1i pick out the ith row of the matrix of the moving average coefficients, which is denotedby Φi,τ .
Variance decomposition (15) models the extent to which the changes in expectations
about the h-step-ahead path of unconventional policy indicator i at the time of the BOJ’s
public statement (represented by the denominator) are attributed to the associated unconventional policy shock j, denoted by ϵMP(where the contributed component of j isj,trepresented by the numerator). The MFEV approach to identify unconventional monetary policy shocks (i.e., quantitative and qualitative policy shocks) maximizes the variancedecomposition by mapping unconventional policy indicator i to the associated unconventional policy shock j. This identification is based on the legitimate assumption that when
the central bank announces its plans of action on two unconventional policy instruments—
the change in the size of its balance sheet (MB) and the purchase of more or less unconventional assets (COMP)—agents will hear the announcement and update their expectations
about the paths of the policy instruments accordingly. In other words, as long as the central
bank keeps the its promises (at least for operations on the size of its balance sheet and thecomposition of its assets) and secures the agents’ trust in the central bank’s announcement,20

the identification of the associated two unconventional policy shocks based on the MFEV
approach allows us to reveal the actual movements in the two balance sheet instruments
after the unconventional policy shocks arrive.
Also note that unlike an existing identification scheme such as a recursive restriction
or a sign restriction, the MFEV approach makes no assumption on how the size andcomposition of the central bank’s balance sheet will respond after the bank makes a policy
decision. This approach only assumes that agents revise their expectations of the path of apolicy indicator according to the scheduling actions that the central bank announces withregard to the indicator. In this sense, the MFEV approach is more agnostic and flexible thanthe existing identification approach in identifying a particular type of policy shock relating
to monetary policy indicators. Given the specific movements of the quantitative andqualitative policy measures following policy changes (see Subsection 2.2), this approachallows us to prevent misspecification of the quantitative and qualitative monetary policy
shocks.
To identify the quantitative, qualitative, and short-term policy rate shocks with the
MFEV approach, we begin by identifying the quantitative monetary policy shock, ϵQNt ,satisfying the following conditions:dˆ1 = arg max Ω11 (h),
d1

(16)

s.t.
′

d1 d1 = 1.

(17)

Constraint (17) (d1 have unit length) ensures that d1 is the first column vector belonging toorthonormal matrix D. After obtaining dˆ1 by solving the above maximization problem, we
calculate the impulse responses of the VAR variables to the quantitative monetary policy
shocks using the estimated impact vector R̃MP dˆ1 .
Next, we identify the qualitative and conventional monetary policy shocks. Specifically,
we identify the qualitative monetary shocks ϵQL
by solving the following maximizationt

21

problem:dˆ2 = arg max Ω22 (h),
d2

(18)

s.t.
′

d2 d2 = 1,′(19)

d2 d1 = 0,(20)

d1 = dˆ1 .

(21)

Constraints (19) and (20) ensure that d2 is the second column vector belonging to orthonormal matrix D. Constraint (21) ensures that the qualitative shock is extracted after thequantitative shock. This implies that, in a qualitative shock with a target level for the monetary base given, the central bank aims to change the composition of its assets through, forexample, an operation twist.23 We can compute the impulse responses to the qualitative
monetary policy shocks using the estimated impact vector R̃MP dˆ2 .
Once two column vectors in the 3 × 3 orthonormal matrix D are given as its first andsecond column vectors dˆ1 and dˆ2 , the third column vector d3 is automatically determined.
In the identification of the conventional short-term policy rate shock ϵSRt , the third column
in the impact matrix RMP representing the impulse responses to the conventional policy rate
shock is obtained as R̃MP dˆ3 . The column vector is orthogonal to the first and second columns
obtained through the above maximization problems, hence the surprise component of themonetary policy explains the small variation in the monetary base and unconventional
assets ratio in the middle- and long-term period. In this sense, our identification strategy
assumes that the central bank controls the short-term policy rate after it determined a planof balance sheet extension and unconventional asset purchases. Section 5 demonstrated
that even when we employ the alternative identification strategy in which a policy ratesetting is assumed to precede a balance sheet setting, the estimated quantitative and23

In the quantitative and qualitative monetary easing from March 2013, the BOJ targets a yearly expansionof the monetary base by 60 to 70 trillion yen (80 trillion yen from October 2014). To meet this monetarybase target, the BOJ purchases exchange trade funds, commercial papers, and long-term government bonds.
Given the fact that the BOJ sets the target level for the monetary base first, the recursive restriction for thequantitative and qualitative shocks is plausible.

22

qualitative policy effects do not depend on those identification strategies.

4

Results for Quantitative and Qualitative Monetary Policy
ShocksIn this section we discuss the empirical results obtained using the monetary policy shocks
identified by the method presented in the previous section. We focus on two unconventional monetary policy shocks, that is, quantitative and qualitative monetary policy
shocks, in particular. In addition to the three monetary policy indicators, the monetarybase (MB), unconventional assets ratio (COMP), and short-term policy rate (SR), we include
five macroeconomic variables in constructing the VAR: two asset market prices, two real
economic variables, and one price indicator. The two asset market prices are the stock
price index (SP) and the 10-year government bond yield (10YJGB). The two real economic
variables are the GDP gap (GGAP) and the difference between the risky assets and safe
assets held by commercial banks (BRISK).24 The risky asset holdings of commercial banks
consist of equity holdings and bank lending, while the safe assets consist of JGBs. The
consumer price index (CPI) is included as the price indicator. See the Appendix for moredetailed information on those variables in the VAR. As discussed in Section 2, our sample
period is from April 1998 to January 2016. The number of lags in the VAR is determined
to be two based on the Schwarz-Bayesian Information Criterion.

4.1

VAR Innovations and Monetary Policy Surprises

In what follows, we report the statistical relevance between the reduced-form VAR innovations and monetary policy surprises. Table 3 shows the estimation results for the system
regression of the reduced-form VAR innovations on the three monetary policy surprises
as expressed in (10).
24

We also use the unemployment rate, shipment of investment goods, and industrial production index inplace of the GDP gap, but the results provided by these alternatives do not differ from the results reported
below.

23

The monetary policy surprises significantly explain only the reduced-form VAR innovations of the short-term policy rate (SR) and the asset prices (SP and 10YJGB), which tells
us that the three asset price variables quickly respond to exogenous policy changes. On
the contrary, the monetary policy surprises explain little of the unconventional policy indicators (MB and COMP), real economic variables (GGAP and BRISK), or price indicator (CPI)
when the shock arrives. We know, therefore, that these latter variables show no immediate
responses to the monetary policy shocks. In particular, the significant explanatory powerof the short-term policy rate and the weaker explanatory power of the quantitative andqualitative policy indicators are consistent with the results of the distributed lag regression(4) (see Subsection 2.2).
From these estimation results, we can interpret the three principal components in linewith previous studies that extracted a principal component (Gürkaynak et al. (2005) and
Swanson (2017)) or emphasized the informational effect of monetary policy (Campbell etal. (2012), Jarociński and Karadi (2020), and Andrade and Ferroni (2020)). For example,
PS1 summarizes the policy surprises that involve the effect of monetary policy on theyield curve, like the longer-term policy factor of Gürkaynak et al. (2005) and Swanson(2017) and the Odyssean forward guidance of Campbell et al. (2012) and Andrade andFerroni (2021), which publicly commit the central bank to a long-term future action. On
the other hand, PS2 summarizes the policy surprises embodying the conventional effectthat causes a negative association between the short-term policy rate and the stock priceindex, as discussed in Jarociński and Karadi (2020). By contrast, PS3 indicates the policy
surprises that involve the positive correlation between the short-term policy rate and thestock price index, like the informational shock of Jarociński and Karadi (2020) and theDelphic forward guidance of Campbell et al. (2012) and Andrade and Ferroni (2021),
which presage near-future economic performance and likely subsequent policy actions.

4.2

Variance Decomposition Analysis

Table 4 presents the variance decomposition of the three monetary policy indicators attributable to each of the monetary policy shocks for h = 0, 12, 24, 36, and 48 months ahead,
which is computed using equation (15) for the MFEV approach.
24

While most of the variance of the monetary base (MB) and unconventional assets ratio(COMP) at h = 0 is attributable to the conventional policy rate shock, the table clearlyshows that most of the variance of the two unconventional policy indicators at h ≥ 12
is explained by their corresponding monetary policy shocks. More concretely: at h ≥ 12
the quantitative shock explains almost all of the variance of the monetary base and thequalitative explains almost all of the variance of the unconventional assets ratio, whileat h ≥ 36 the two types of shock explain the variance of the unconventional assets ratio
equally. This implies that the extension of the BOJ’s balance sheet is realized slowly andgradually and that its medium- and long-term purchasing of unconventional assets isdetermined in accordance with the balance sheet extension.
The quantitative and qualitative monetary policy shocks appear to explain most of
the variance of the short-term policy rate (SR). The variance of the policy rate at h = 0
is almost fully attributable to the qualitative policy shock, but most of the variance ath ≥ 12 is accounted for by the quantitative policy shock. Our finding that the short-term
policy rate shock accounts for much rather than little of the variance is consistent with theidentifying assumption that the central bank’s planning balance sheet operations dictates
its control of the short-term policy rate. In Subsection 5.1, we will discuss a case in which
the central bank’s policy rate control comes before the balance sheet size and composition
are controlled.

4.3

Impulse Response Analysis

In this subsection we describe the estimated impulse responses to the exogenous monetary
policy shocks. Figure 2 outlines the estimated impulse responses to the quantitative policy
shock, the qualitative policy shock, and the short-term policy rate shock.25
25

We employ a parametric bootstrap to compute the confidence intervals in the following; we randomly
replace the pair of the three common factors and the VAR residuals, and then generate a bootstrap sample ofeach macroeconomic variable by substituting the resampled VAR residuals into the estimated VAR model.
After obtaining VAR residuals by re-estimating the VAR model with the bootstrap sample of macroeconomic
variables, we use the re-estimated VAR residuals and the resampled common factors to calculate impulse
responses to the monetary policy shocks through equations (10)-(21). We repeat this procedure 1000 times
to compute one standard error confidence band. Following Jentsch and Lunsford (2016), we also use themoving-block resampling to compute confidence intervals for robustness check. Confidence intervals basedon the moving-block resampling with the block length of twelve is not so different from those based on our25

Effects of Quantitative Shocks
As the left column of Figure 2 shows, the quantitative easing shock leads to a gradualand continuous increase in the monetary base (MB) without affecting it immediately. The
monetary base reaches a peak at around one year following the quantitative easing shock.
We can thus identify the quantitative shock as an anticipated shock linked to the expansionof the balance sheet (i.e., agents expect the monetary base to reach its target level soonafter the BOJ announces its new target). The quantitative easing shock also leads to a slow
increase in the unconventional assets ratio (COMP), which clearly shows that the BOJ tends
to increase its unconventional assets more than its conventional assets when expanding
its balance sheet.
In estimating the responses of the nominal interest rates, we find that the short-term
policy rate (SR) and long-term nominal interest rate (10YJGB) both fall immediately, but thatthe latter falls more. The immediate response of the long-term interest rate implies thata quantitative easing shock has a policy duration effect that decreases long-term interest
rates immediately by working as a signal about the future path of policy rates.
The quantitative easing shock confers no favorable effects on the stock price (SP) or
the commercial bank holdings of risky assets (BRISK), so both of the variables decline. We
can infer, from the estimation results, that the quantitative easing shock was in no way
effective in bringing about a portfolio rebalance where financial institutions with safer
assets could be expected to lend more and increase the purchase of relatively risky assets,
including stocks. Rather, the quantitative easing shock appeared to merely result in atight supply/demand balance in the long-term Japanese government bond market or tochange the market’s expectations on the duration of the zero interest rate policy (Okina
and Shiratsuka (2004) and Ugai (2007)).
Consistent with this inference, the quantitative easing shock brought about less thanfavorable effects on the GDP gap (GGAP) and price indicator (CPI), as well.26 Given that
this shock significantly decreases the long-term nominal interest rate and generates aflattening yield curve, we can infer that the interest rate channel through the decrease insimple bootstrap resampling with the block length of one.
26
Hayashi and Koeda (2019) found that exiting from the quantitative easing policy is expansionary if theactual-to-required reserve ratio is not unduly large.

26

the long-term nominal interest rate in response to quantitative easing fails to bring about
the intended effects under Japan’s unconventional monetary policy regime.
Effects of Qualitative Shocks
As we see in the middle column of Figure 2, the qualitative easing shock has a significant
effect on the unconventional assets ratio (COMP) without imparting a contemporaneous
impact. More concretely, the unconventional assets ratio peaks almost six months later.
On the contrary, the monetary base (MB) shows no significant response to the qualitativeeasing shock.
In contrast to the quantitative easing shock, the qualitative easing shock leads to a
substantive increase in the stock price (SP), an increase in the long-term nominal interest
rate (10YJGB), and a persistent increase in the commercial bank holdings of risky assets(BRISK). Unlike the effects we find when the BOJ expands its balance sheet, the larger
purchases of unconventional assets under the qualitative easing policy increase the stock
price and generated a steepening yield curve. Qualitative easing also prompts financial
institutions to increase the purchase of unconventional assets and lend more in portfolio
rebalancing.
For the estimated responses of the other real economic variables, the GDP gap (GGAP)
and price indicator (CPI) both increase significantly.
Policy Rate Shock Effects under Planning the Balance Sheet Structure
As the right column of Figure 2 shows, a short-term policy rate shock that significantly
decreases the policy rate leads not to a change in the monetary base (MB), but to an
immediate decrease in the unconventional assets ratio (COMP). This effect tells us, as
discussed in Section 3, that our identification scheme assumes that the BOJ determines
its evolution of balance sheet structure before an actual change in the short-term policy
rate. Once the BOJ decides to decrease (increase) the policy rate, however, the central
bank implements the policy rate control temporarily by purchasing (selling) conventional
assets and selling (purchasing) unconventional assets and by increasing the prices of
conventional (unconventional) assets, with the balance sheet size unchanged.
In such identification of the short-term policy rate shock, the long-term nominal interest
27

rate (10YJGB) immediately and substantially falls and the yield curve temporarily flattens.
By contrast, the stock price (SP), the commercial bank holdings of risky assets (BRISK), theGDP gap (GGAP), and the price indicator (CPI) do not significantly respond to the shortterm policy rate shock when the balance sheet is of a given size. These neutral effects of thepolicy rate shock on the stock price and real economy differ from those demonstrated in the
VAR literature for conventional monetary policy effects.27 This difference suggests that if
the central bank keeps the size of its balance sheet unchanged in controlling the short-term
policy rate (as seen in the normalization of the U.S. monetary policy since December 2015),
the stock price and real economy could be neutral to the policy rate change.
In Section 5, we extend our impulse response analysis by conducting a robustness checkbased on the alternative identification scheme in which the central bank sets its policy rate
before it sets its balance sheet structure.

5

Unconventional Monetary Policy Effects and Robustness

In this section we conduct a robustness check based on an alternative identification strategy
in which the central bank is assumed to control the short-term policy rate before it plans
its balance sheet structure. We also explore several implications of the unconventionalmonetary policy effects on the macroeconomy by focusing on comparisons with the existing VAR studies and hypotheses to be further investigated on the topic of unconventional
policy effects.

5.1

Alternative Identification and Robustness

To conduct a robustness check based on the alternative identification strategy, we introduce
two additional identifying constraints besides constraints (16) to (21):r̃31 d1,1 + r̃32 d2,1 + r̃33 d3,1 = 0,
27

(22)

See e.g., Bernanke and Blinder (1992), Christiano et al. (1996), and Bernanke and Mihov (1998) for details
on the U.S. conventional monetary policy. See e.g., Miyao (2000; 2002), Nakashima (2006), and Shibamoto(2016) for details on the Japanese conventional monetary policy.

28

and
r̃31 d1,2 + r̃32 d2,2 + r̃33 d3,2 = 0,(23)

where r̃31 , r̃32 , and r̃33 indicate the element of the third row vector of R̃MP related to thepolicy rate response to monetary policy shocks. The two constraints ensure that the central
bank controls its policy rate before it controls its evolution of balance sheet. Put differently,
the quantitative and qualitative monetary policy shocks have no immediate impacts on the
short-term policy rate.28 Figure 3 reports estimated impulse responses to the quantitative,
qualitative, and short-term policy rate shocks based on this alternative identifying scheme.
The left and middle columns of Figure 3 clearly show that although the quantitativeand qualitative easing shocks reflect no instantaneous response of the short-term policy
rate in accordance with the predetermined policy rate assumption, the unconventional
policy easing shocks generate the same patterns of impulse responses as those basedon the predetermined balance sheet assumption (see Subsection 4.3). In this sense, ourunconventional policy effects are robust irrespective of those different identifying schemes.
Unlike the policy rate shock under planning balance sheet operations, under the assumption that the BOJ sets policy rate before determining the balance sheet size andcomposition, the policy rate shock basically produces impulse responses similar to thosedemonstrated in the VAR literature for conventional monetary policy effects (see the rightcolumn of Figure 3). In this sense, the policy rate control that comes before the balance sheet
setting can be seen as the conventional monetary policy even in an extremely low-interest
regime, while the policy rate control subject to the long-term balance sheet control can beseen as an unconventional policy option in the central bank’s balance sheet operations.
This contrast between the two types of short-term policy rate shocks can also be observed in their different results for the variance decomposition of the three monetary
policy indicators. Table 5 reports results for the variance decomposition attributable to28
More precisely, once the policy rate shock is determined before the quantitative and qualitative policy
shocks under identifying restrictions (22) and (23) and the quantitative shock is determined before the qualitative shock by solving maximization problem (16) and obtaining dˆ1 , the qualitative shock is automaticallydetermined without solving maximization problem (21). As far as this identification strategy in maintained, however, such automatic determination of the qualitative shock is formally equivalent to solving
maximization problem (21).

29the associated monetary policy shocks. Compared with the policy rate control subject tothe balance sheet control (see Subsection 4.2), the assumption that the BOJ sets the policy
rate prior to setting other policy tools generates the estimation result showing substantive
increases in the contribution of the short-term policy rate shock to the variance of each of
the policy indicators.
In this type of identification of the policy rate shock, Figure 3 shows that the policy
easing that immediately decreases the short-term policy rate (SR) leads to an increasein both the monetary base (MB) and unconventional assets ratio (COMP). The estimatedimpulse responses of the stock price (SP) remain less than significant for about one year
after the policy rate shock, then increase steadily from the end of the first year to the endof the third year. The long-term nominal interest rate (10YJGB) immediately decreases andthe long-short spread narrows, responding to the policy rate shock. From the positive
responses of the commercial bank holdings of risky assets (BRISK), we can infer that thepolicy easing shock causes a portfolio rebalance. The policy rate shock leads to increases inboth the GDP gap (GGAP) and price indicator (CPI), though the former initially decreases
in the first few periods. The GDP gap peaks after the price indicator begins to increase, at
about the one year point after the policy rate shock.

5.2

Comparison with Unanticipated Policy Shocks

As discussed in the Introduction, a number of previous studies have assumed that monetary aggregates such as the monetary base and excess reserves represent the central bank’s
policy stance. These studies have thus employed the standard recursive VAR approachor extensions of that approach, such as regime-switching or time-varying parameter VAR
models. Another VAR approach employed in previous studies assumes that unconventional monetary policy shocks can be represented collectively as a single unobservablepolicy shock. This other VAR approach, therefore, imposes sign restrictions on the instantaneous responses of macroeconomic variables to a single policy shock or imposes
heterogeneous variance restrictions on the intensity of structural shocks, including single
policy shocks.
Regardless of the difference in identification strategy, the exogenous components of30

the unconventional monetary policy identified in the previous studies are characterized
as “unanticipated” unconventional monetary policy shocks. We describe such a shock as“unanticipated” because, in contrast to an “anticipated” policy shock, it provides no priorinsight into the current and future paths of the monetary base and unconventional assets
ratio. In addition, none of those previous studies took our approach of using the composition of the central bank’s assets as an unconventional monetary policy indicator. In
this subsection we compare the macroeconomic effects of our anticipated unconventional
policy shocks (reported in Subsection 4.3) with the effects of our unanticipated unconventional policy shocks. For the comparison, we adopt the standard recursive VAR approachbased on the Cholesky decomposition to extract an unanticipated unconventional policy
shock. We employ the recursive approach because the sign restrictions (Schenkelberg and
Watzka (2013)) and the heterogeneous variance restrictions (Shibamoto and Tachibana(2017)) yield qualitatively the same impulse responses as the recursive ones when using
the data from Japan.29
In doing so, we focus on four dimensions in particular: 1) effects on the monetary base,2) effects on the interest rate of long-term government bonds, 3) effects on real economic
activity, and 4) the exogeneity of unconventional monetary policy shocks.
Figure 4 shows estimated impulse responses to the unanticipated monetary base shock(left column) and composition shocks (right column) obtained using the Cholesky decomposition in the eight-variable VAR system, respectively.30 We find that the evaluation of
unconventional policy effects heavily depends on whether quantitative and qualitativeeasing shocks are identified as anticipated (Figure 2) or unanticipated shocks (Figure 4).
29

Schenkelberg and Watzka (2013) and Shibamoto and Tachibana (2017) focused on the BOJ’s quantitative easing period from 2001 to 2006. We found, however, that the three alternative approaches yielded
qualitatively the same impulse responses across sample periods.
30
More specifically, in the eight-variable VAR system we order the variables in the conventional manner:
output and prices (GGAP, CPI), policy indicators (MB, COMP, SR), and the four financial variables, including
the stock price index (SP). For comparison with the anticipated monetary base and composition shocks (i.e.,the quantitative and qualitative shocks defined in Section 3), the eight-variable VAR system imposes arecursive restriction for the three monetary policy indicators, in which three unanticipated monetary shocks
are determined in the order of an unanticipated policy shock to the monetary base (MB), the unconventional
assets ratio (COMP), and the short-term policy rate (SR) (see Section 3). We find that the impulse responses
yielded by the short-term policy rate shock (related to the SR) identified in this recursive VAR system aresubstantially the same as the impulse responses shown in Figure 2. Hence, we do not report them here.

31Effects on the Monetary Base
The difference in the dynamics of monetary aggregates conditioned on unconventional
policy shocks is the most notable when comparing the anticipated and unanticipatedshocks. Existing VAR studies identifying unconventional monetary policy shocks as unanticipated shocks have demonstrated a contemporaneous impact on monetary aggregates.31
Indeed, as shown in the left column of Figure 4, the unanticipated monetary base shockleads to an immediate increase in the monetary base (MB), while the anticipated monetarybase shock (i.e., our quantitative easing shock) leads to a gradual increase (see Figure 3).
Also note that, as emphasized in Subsection 3.3, we do not impose any restrictions otherthan the maximization of the forecast error of the current and future paths of the monetarybase when identifying the anticipated monetary base shock by imposing restrictions (16)and (17). Hence, our strategy for identifying the anticipated monetary base shock lets thedata speak more for unconventional policy effects on the monetary base, compared with
our strategy for identifying the unanticipated monetary base shock. Given this point, thenon-contemporaneous response and the gradual increase of the monetary base are themost distinguished features of the anticipated monetary base shock and reflect more of
the actual dynamics of the monetary base: the targeted monetary base level is achieved
gradually after a policy change announcement, but not abruptly in the announcement.
We can observe the same tendency in the difference between the anticipated composition shock (i.e., our qualitative easing shock) and the unanticipated composition shock: theunanticipated composition shock has an contemporaneous impact on the unconventional
assets ratio (COMP), as shown in Figure 4, while the anticipated composition shock does
not, as shown in Figure 2.
Effects on Long-term Government Bond Yields
Some VAR-based studies of the unconventional monetary policy, particularly in the
UK and the US, have emphasized the policy’s causal effect on long-term bond yields (e.g.,31

Such a contemporaneous impact on monetary aggregates can be observed in Japanese VAR-based
studies: e.g., Iwata and Wu (2006) for M1, and Honda et al. (2013), Schenkelberg and Watzka (2013), Kimura
and Nakajima (2016), Shibamoto and Tachibana (2017), Miyao and Okimoto (2017), and Hayashi and Koeda(2018) for bank reserves. In one VAR-based study on the U.K. and U.S., Weale and Wieladek (2016) showed acontemporaneous impact on asset purchases. Gambacorta et al. (2014) showed a contemporaneous impact
on the total assets of central banks in industrialized countries.

32

Kapetanios et al. (2012), Wright (2012), Baumeister and Benati (2013), and Weale andWieladek (2016)).32 Their motivation stems from the assumption that unconventional policy interventions in the Treasury market would lower the long-term bond yields and thenspur real economic activity. Under this assumption they identify an unanticipated policy
shock, thereby demonstrating that a stimulative unconventional policy shock would lower
the long-term bond yields and narrow the long-short spread of government bonds. Such
results for an unanticipated policy shock in the U.K. and U.S. are observed for the anticipated monetary base shock (i.e., our qualitative policy shock) and the two unanticipatedshocks in Japan, as shown in the impulse responses of the 10-year government bond yield(10YJGB) (see Figures 2 and 4), but not for the anticipated composition shock (i.e., ourqualitative policy shock).
Note that while the two anticipated shocks, our quantitative and qualitative easing
shocks, both have substantial impacts on the long-term government bond yield (10YJGB)
and long-short spread of the long-term yield and short-term policy rate (SR), the dynamics
are quite different: the quantitative easing shock has an immediate and persistent effect on
the long-term government bond yield and causes a contraction of the long-short spread,
while the qualitative easing shock has a slow and less persistent effect and generates asteepening yield curve. The unconventional policy effects on the yield curve depend on
the policy tools, as well as the anticipated and unanticipated policy shocks.
Effects on Real Economic Activity
Our quantitative easing and qualitative easing shocks exert opposing effects on not
only the long-term government bond yield, but also real economic activity. While thequantitative easing shock has contractionary effects on output (GGAP), prices (CPI), andthe risk appetite of banks (BRISK), the qualitative easing shock has expansionary effects(left and middle columns of Figure 2). These effects imply that a decrease in the long-term
government bond yield stemming from the expansion of the monetary base cannot be
presumed to be associated with a rise in real economic activity (Okina and Shiratsuka
32

To examine unconventional policy effects on the long-term bond yields, Wright (2012) employed theheterogeneous variance restriction approach, whereas Kapetanios et al. (2012) and Baumeister and Benati(2013) used the sign restriction approach. Weale and Wieladek (2016) employed four alternative approaches,
including the recursive and sign restriction approaches.

33

(2004) and Ugai (2007)), and that unconventional policy effects on real economic activity
are likely to be heavily dependent on the policy tools.33
Both the anticipated and unanticipated composition shocks have expansionary effects
on output, prices, and the risk appetite of banks, though the magnitude and persistencyof the effects differ between the two composition shocks (right columns of Figures 2 and
4). Regarding the quantitative policy tools, the anticipated monetary base shock has nosuch expansionary effects (left column of Figure 2), whereas the unanticipated base shock
has expansionary effects about one year after the shock arrival (left column of Figure 4).34
Exogeneity of Unconventional Monetary Policy Shocks
Why the unanticipated monetary base shock has expansionary effects remains an open
question. To explore this question, we examine the associations among the unanticipatedmonetary policy shocks and global economic variables out of the eight-variable VAR.
The linkages between the Japanese economy and global economy may endogenously
determine the unanticipated changes in the policy indicators, including the monetarybase. From this analytical viewpoint, we conduct the following system regression of theunanticipated monetary policy shocks on global economic factors:u
UPt = RGF
u GFt + et ,(24)

where UPt denotes the vector variable composed of the unanticipated monetary policy
shocks in month t obtained from the eight-variable recursive VAR, and GFt denotes a
33

Okina and Shiratsuka (2004) empirically demonstrated that although the BOJ’s quantitative easing was
effective in stabilizing market expectations about the future path of short-term interest rates, and thereby
brought longer-term interest rates down, these effects were not transmitted to the whole economy in Japan.
Ugai (2007) similarly pointed out that the BOJ’s quantitative easing had only a limited effect on raisingaggregate demand and prices, though it succeeded in lowering the yield curve.
34
Previous VAR-based studies emphasizing the expansionary effects of the BOJ’s quantitative easing policy
focused on the quantitative easing from March 2001 to March 2006 (e.g., Honda et al. (2013), Schenkelberg
and Watzka (2013), Shibamoto and Tachibana (2017), and Hayashi and Koeda (2019)). We also examined
the impulse responses to the anticipated and unanticipated monetary base shocks obtained during thequantitative easing period from 2001 to 2006. While we found that the anticipated monetary base shock
still yielded a gradual increase in the monetary base and exerted contractionary effects on real economic
activity even in the quantitative easing period, the unanticipated monetary base shock and contemporaneous
increase in the monetary base had expansionary effects, as demonstrated in the previous studies. We also
found that both the anticipated and unanticipated composition shocks had expansionary effects in thequalitative easing period.

34

vector variable composed of five global economic variables expected to have substantive
effects on the Japanese economy: the oil price (OIL), global index of industrial production(GIP), U.S. economic policy uncertainty index (USEPU), U.S. TED spread (USTED), andfederal funds rate (FFR)) .
In another exercise we examine whether our identified monetary policy shocks can be
determined from the global economic variables using the following system regression:p

PSt = RGF
p GFt + et ,(25)

where PSt represents the three monetary policy surprises, that is, the basis of our qualitative, quantitative, and short-term policy rate shocks.
Table 6 reports the estimation results for the two system equations (24) and (25). As
the left panel of Table 6 shows, the unanticipated policy rate shock (USR) is not associated with any of the global economic factors. The unanticipated monetary base shock(UMB), on the other hand, is negatively and positively associated with the global index
of industrial production (GIP) and the U.S. economic policy uncertainty index (USEPU),
respectively. This indicates that the unanticipated monetary base shock increases as an
endogenous response to the deterioration in the global economic condition. The unanticipated composition shock (UCOMP) is also significantly associated with the U.S. economic
policy uncertainty index, though in contrast to the unanticipated monetary base shock, itscorrelation with the index is negative. We find, therefore, that the unanticipated composition shock endogenously increases in response to the improvement in the global economic
condition.
In contrast, as the right panel of Table 6 shows, none of the monetary policy surprises
are significantly associated with the global economic variables at the five percent level ofsignificance. This estimation result ensures that our anticipated monetary policy shocks
are exogenous to global economic shocks left out of the VAR system.
The above analysis suggests that the simple use of the unanticipated changes in theunconventional monetary policy indicators (i.e., the monetary base and unconventional
assets ratio) can lead to biased estimates of the policy effects (see Gertler and Karadi

35

(2015) for details on the U.S. conventional monetary policy effects). In particular, theunanticipated monetary base shock, which was mainly utilized by Japanese VAR-based
studies to measure unconventional monetary policy effects, is negatively associated withthe global economic condition, which is not controlled in the VAR model. Hence, unlike
the anticipated monetary base shock, the unanticipated shock captures the global economic condition and accordingly cannot be considered a “pure” monetary policy shock.
Given this negative association with the global and U.S. economic conditions, the favorable
effects of the unanticipated monetary base shock presumably arise from the coordinationof central banks around the world, along with the global spillover effects from that coordination, such as the provision liquidity to malfunctioning financial markets. This may be
one reason why the unanticipated monetary base shock has expansionary effects on real
economic activity.

5.3

Importance of Monetary Policy Surprises

In this subsection, we briefly discuss the importance of combining the monetary policy
surprises with the MFEV approach in identifying the quantitative and qualitative policy
shocks.35 To examine the importance of using the monetary policy surprises, or the threeprincipal components, as external instruments, we apply the MFEV approach directly tothe variance-covariance matrix of the reduced-form VAR innovations ut , but not to thatof fitted values generated from system regression (10). Thus, we found that the resulting
impulse responses show some implausible size of the effect and unreasonable moves of themonetary policy instruments, implying that the identification based on the VAR residuals
would be contaminated by the endogeneity problem.36

5.4

Hypotheses about Unconventional Monetary Policy Effects

We have thus far found that our quantitative easing shocks, or the anticipated monetarybase shocks, have no favorable effects on the real economy, although they do precipitate
35

Estimated impulse responses discussed in this subsection are available upon request.
See also Kim (2017), Lakdawala (2019), and Kim et al. (2020) for other recent approaches to identifying
multiple policy shocks using external instruments in a VAR setting.
36

36

decreases in the long-term nominal interest rate, as expected by the BOJ. On the contrary,
our qualitative easing shocks, or the anticipated composition shocks, cause favorable
effects not only on the long-term interest rate, but also on real economic activity. In thissubsection we draw from these findings to propose three inter-related hypotheses about
the unconventional policy effects, to explore in future research.
One hypothesis holds that the ineffectiveness of quantitative easing shocks can be
explained by their effect in raising concern about the future fragility of the real economy. According to Romer and Romer (2000), Ellingsen and Söderstrom (2001), Claus andDungey (2012), Campbell et al. (2012), Nakamura and Steinsson (2018), and Munakata
et al. (2019), monetary policy actions provide the public with signals of a central bank’s
information. If the quantitative easing by the BOJ worked as a signal presaging future
decreases in output and inflation, this signal would suppress firm investment and wage
growth.
The second hypothesis involves economic uncertainty and its effect in instilling caution
in real economic activity. Bekaert et al. (2013) used stock market option-based implied
volatility data, or VIX data, to demonstrate that conventional policy easing through reductions in the short-term interest rate decreases economic uncertainty, which in turn leads
to favorable effects on the real economy (see also Aastveit et al. (2017) and Creal and Wu(2017)).37 If the quantitative easing shock elevates economic uncertainty while the qualitative easing shock contributes to its reduction, the difference in the estimated responsesof the real economic variables to the two unconventional policy shocks can be explained
along this line. Figure 5 reports estimated impulse responses of two uncertainty indexes,
Japan’s monetary policy uncertainty index (JMPU) and the volatility index Japan (JVIX), to
the quantitative and qualitative monetary policy shocks. As this figure clearly shows, thequantitative easing shock can be expected to increase both the uncertainty indexes, while
the qualitative easing shock can be expected to decrease them.38
37

Gürkaynak and Wright (2012) pointed out that the instability in investors’ inflation expectations could
stem from a lack of central bank credibility, a problem that might drive a wedge between actual and perceived
inflation targets.
38
We found that the short-term policy rate shock under planning balance sheet operations did not affectthe two uncertainty indexes, while the policy rate shock followed by the quantitative and qualitative policy
shocks decreased them. This result is consistent with the findings reported in Subsections 4.3 and 5.1.

37

The third hypothesis is based on the growing risk of a government debt crisis comparable to an aggressive expansion of the central bank’s balance sheet. Some theoretical studies
have pointed out that such a sovereign debt crisis risk can lower not only the government
bond yield, but also the output (e.g., Kozlowski et al. (2015)), the rate of output growth(e.g., Kobayashi and Ueda (2018)), and the price level (e.g., Saito (2020)). Given that Japan’s
gross government debt exceeds 200% of the nominal GDP, this third hypothesis, which
conjectures a loss of market confidence in government debt that forces the government tocollect large tax revenues, seems to convincingly account for the contractionary effects of
the quantitative easing.39

6

Conclusion

Previous VAR-based studies have evaluated the central bank’s balance sheet operationsin an unconventional monetary policy by assuming either that the central bank uses
monetary aggregates such as the monetary base and excess reserves as unconventionalmonetary policy measures, or that the underlying unconventional monetary policy shocks
can be captured collectively by a single monetary policy shock. Hence, the previous
studies that make these assumptions neglect to distinguish between quantitative andqualitative monetary policy shocks, which prevents them from correctly disentangling thepolicy effects. In the present study we proposed a new method to separately identify thequantitative and qualitative monetary policy shocks, as well as the short-term policy rate
shock, using the unconventional monetary policy the Bank of Japan has kept in place since
1999.
Rather than assuming how a policy indicator responds to an associated policy shock,our method for identifying shocks makes only one assumption, namely, that agents revise
their expectations about the path of a policy indicator in accordance with the central
bank’s announcement of its scheduling action for the indicator. In this sense, our method
39

Cúrdia and Woodford (2011) theoretically demonstrated that while quantitative easing is likely to beineffective, qualitative easing due to the central bank’s targeted asset purchases can be effective whenfinancial markets are disrupted (see also Chen et al. (2012)). In terms of the risk-taking of Japanesecommercial banks in lending, Nakashima et al. (2020) empirically showed that the BOJ’s qualitative easing
stimulated bank risk-taking, while the quantitative easing did not.

38

is agnostic in identifying a particular type of policy shock relating to a monetary policy
measure. By demonstrating that the quantitative and qualitative policy measures differ
from the policy rate, with neither showing any immediate responses to the central bank’s
announcement, we have determined that the existing identification methods that cause
unconventional policy measures to immediately respond are unsuited for identifying theassociated policy shocks.
By defining the two unconventional policy shocks as anticipated shocks, we observein a robust manner that the qualitative easing shock, which involves a gradual increase in
the ratio of the BOJ’s unconventional asset to its total assets, yields expansionary effects,
whereas the quantitative easing shock, which involves a gradual increase in the size of
the BOJ’s balance sheet, does not. In future research we will explore why these two
unconventional policy shocks yield such different policy effects along the lines suggested
in this paper.

Appendix I: Variable Definitions• Monetary base (MB): seasonally adjusted series, monthly average, retrieved from the
Bank of Japan statistics.
• Composition ratio of the BOJ’s unconventional assets to total assets (COMP): the
BOJ’s unconventional assets are defined as the sum of the BOJ’s holdings of Japanese
Government Bonds, commercial papers (from February 2009), corporate bonds (fromMarch 2009), asset-backed securities (from July 2003 to September 2006), stocks held
as trust property (from November 2002), Index-Linked Exchange Traded Funds held
as trust property (from December 2010), and Japan Real Estate Investment Trusts
held as trust property (from December 2010) (end of month), retrieved from the
Bank of Japan statistics. The BOJ’s total assets (end of month) retrieved from the
Bank of Japan statistics.
• Short-term policy rate (SR): uncollateralized overnight call rate, monthly average,
retrieved from the Bank of Japan statistics.
39

• Stock price index (SP): Nikkei Stock Average (Nikkei225) index (end of month) retrieved from NIKKEI NEEDS FINANCIAL QUEST.
• Long-term government bond yield (10YJGB): 10-year Japanese government bondyields (end of month) retrieved from NIKKEI NEEDS FINANCIAL QUEST.
• Commercial bank holdings of risky assets (BRISK): difference between the risky assets
and safe assets held by commercial banks. Risky assets are defined as the sum of
the commercial bank holdings of bank loans, stocks, corporate bonds, and foreign
securities (end of month) retrieved from the Bank of Japan statistics. Safe assets
are defined as the commercial bank holdings of Japanese government bonds (end ofmonth), retrieved from the Bank of Japan statistics. We obtain seasonally adjusted
series using the Census X-12.
• GDP gap (GGAP): Quarterly GDP gap series retrieved from the Bank of Japan statistics
and interpolated to obtain monthly observations.
• Consumer price index (CPI): consumer price index, excluding fresh foods (2015=100),
consumption-tax-adjusted series for the period from April 1998 to December 2014,
retrieved from the Ministry of Internal Affairs and Communications. We obtainseasonally adjusted series using the Census X-12.
• Oil price (OIL): Crude Oil Prices: West Texas Intermediate (WTI) (monthly average)
retrieved from FRED.
• Global index of industrial production (GIP): World Industrial Production, excluding
construction (Import-Weighted, 2010=100), retrieved from HAVER ANALYTICS.
• U.S. economic policy uncertainty index (USEPU): A news-based policy uncertainty
index retrieved from the following website: http://www.policyuncertainty.com/.
• U.S. TED spread (USTED): the spread between the 3-Month LIBOR based on US
dollars and 3-Month Treasury Bill (monthly average) retrieved from FRED.

40• Federal funds rate (FFR): effective federal funds rate (monthly average) retrieved from
FRED, shadow federal funds rate series of Wu and Xia (2016) from January 2009 toNovember 2015, retrieved from Jing Cynthia Wu’s website: https://sites.google.com/
view/jingcynthiawu/shadow-rates.
• Japan’s monetary policy uncertainty index (JMPU): Japan’s Monetary Policy Uncertainty Index constructed by Arbatli et al. (2022), retrieved from the following website:
http://www.policyuncertainty.com/.
• Japan’s volatility index (JVIX): Volatility Index Japan, retrieved from the following
website: http://wwwmmds.sigmath.es.osaka-u.ac.jp/en/activity/vxj.php (monthly average).

Appendix II: Development of Monetary Base and Unconventional Asset Ratio
We plot the two policy instruments of the monetary base (MB) and the unconventional
asset ratio (COMP), focusing on the two distinct periods: the first half of the 2000s for the
QE and the 2010s for the QQE in Figure A1. The monetary base and risky asset ratio haveenough variation to allow us to identify two different shocks, although there are someperiods when the two indicators share a trend. In fact, as the left-hand panel shows, when
the first QE was implemented in the 2000s, the two indicators show a large variation.
For example, when the BOJ started the QE, the risky asset ratio increased swiftly, but themonetary base increased gradually. On the other hand, when the BOJ exited the QE in2006, the MB decreased while the COMP increased. This is partly because the reduction of
the balance sheets was mainly conducted through the reduction of holdings of short-term
government debts. From these examples, we can infer that the MB and COMP reflect
different information about the monetary policy stance. In addition, in the 2010s, the MB
and COMP show different changes depending on the policy actions that the BOJ took.
For example, before the introduction of the QQE in 2013, the COMP increased over time
due to an increase in the purchase of long-term government bonds, ETFs and real estate41

investment trusts. During this period, the MB only gradually increased. On the other
hand, after 2015, both the MB and COMP have increased almost monotonically. However,even after the introduction of the QQE, especially in 2014, the COMP had remained almost
flat, while the MB continued to increase substantially. These observations clearly showthat the COMP and MB do not always share a trend; rather, they show a wide variation
depending on periods.

Appendix III: Estimated Monetary Policy Shocks
The estimated shocks are shown in Figure A2. There are several observations worth
mentioning. First, as Figure A2 shows, the quantitative shock (QN) saw a significant
negative value when the BOJ exited from the zero interest rate policy in August 2000. In
addition, in March 2001, when the BOJ decided to implement the quantitative easing (QE),
the QN shock reached its peak. The QN also captures the timing when the BOJ exited
the QE and increased the policy rate in July 2006. After the GFC, the BOJ expanded its
balance sheets immediately and continued the expansion of QE and QQE in the 2010s.
In this period, the QN shocks stayed positive on average. As for the qualitative (QL)
shock, there was a sharp hike in the QL shock in March 2001 when the BOJ started theQE. In the monetary policy meeting in March 2001, the BOJ also decided to increase thepurchasing amount of long-term bonds, which we categorized as unconventional assets.
Therefore, the QL shock tracks the change in asset composition successfully. In addition,in October 2008, the QL shock dropped and increased substantially in the next period. Thisswing reflects the aggressive monetary policy stance of the BOJ to address the turmoil in
the GFC. In the meeting on October 14, 2010, the BOJ decided to increase especially theprovision of short-term liquidity. This policy decreased the share of unconventional assets
temporarily. However, in the later meetings, the BOJ decided to increase the purchasing
amount of longer-term bonds, which is reflected in the hike in the QL shock. Moreover,in April 2013, when the BOJ announced the introduction of QQE, the QL shock increased
substantially. In the meeting, the BOJ decided to increase the purchase of ETFs andother risky assets. Overall, the development of the estimated shocks indicates that our42identification methodology works well.

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50

Table 1: The Number of Common Factors Underlying the Changes
in Financial Market at the MPM

Number of Factors: k

0

1

2

3

BN (k)
AH(k)

4

2.98

2.94

3.06

2.89

2.95

n.a.

1.05

0.89

1.09

0.52

Notes: BN (k) denotes the Bai and Ng (2002) information criteria, deﬁned as follows;(
BN (k) = log(V (k)) + k

n+T
nT

)

(
log

nT
n+T

)
,

where n is the number of variables in factor model (1): n = 12. T is the number ofobservations. V (k) is the sum of squared residuals divided by nT . AH(k) denotes the Ahn
and Horenstein (2013) information criteria, deﬁned as follows;
AH(k) =log(V (k − 1))/ log(V (k)).
log(V (k))/ log(V (k + 1))

Table 2: Results for the Distributed Lag Regression of EachMonetary Policy Indicator on Monetary Policy Surprises

H=0
H=1
H=2
H=6

Change in Policy Indicator

Level in Policy Indicator

∆ MB

MB

∆ COMP

∆ SR

COMP

SR

2.91

4.57

13.48

1.03

4.89

17.91

[0.41][0.21][0.00][0.79][0.18][0.00]

3.67

5.23

25.83

1.40

5.93

31.71

[0.72][0.52][0.00][0.97][0.43][0.00]

8.54

17.88

33.74

6.45

19.59

47.90

[0.48][0.04][0.00][0.69][0.02][0.00]

20.75

27.74

81.40

26.97

31.06

96.60

[0.47][0.15][0.00][0.17][0.07][0.00]

H = 12

54.41

61.23

67.15

69.19

72.11

107.94[0.05][0.01][0.00][0.00][0.00][0.00]

H = 24126.97527.22288.39171.72470.89394.72[0.00][0.00][0.00][0.00][0.00][0.00]

Notes: The distributed lag regression model is speciﬁed as equation (4). This table showsj
Chi-square statistics (p-values in brackets) resulting from tests of the null hypothesis: rh=0
for all j = 1, 2, 3 and h = 0, · · · , H.

51

Table 3: Results for the Regression of Each VAR Innovation
on Monetary Policy Surprises

VAR Innovation: ut
MB
PS
PS
PS

1

2

3

COMP
†

SR

SP
∗∗

10YJGB
∗∗

BRISK

GGAP

CPI

-2.68

-0.19

-0.95

0.03

-4.79

-0.05

-0.01

0.04

(1.62)(0.22)(0.35)(0.49)(1.71)(0.27)(0.01)(0.19)

∗

∗∗

-1.34

-0.27

0.96

-3.85

-0.93

-0.47

0.01

0.16

(2.20)(0.29)(0.38)(0.82)(1.40)(0.43)(0.02)(0.21)

∗

∗∗

0.47

0.40

0.02

0.18

-1.02

-0.37

(3.88)(0.48)(0.75)(0.83)(2.56)(0.64)(0.02)(0.30)

3.38

1.69

11.54

29.87

10.98

1.96

3.09

1.03

[0.34][0.64][0.01][0.00][0.58][0.38][0.79]

χ2

1.50

2.16

[0.01]

Notes: The regression model is speciﬁed as equation (10). Values in parentheses are robust
standard errors. ∗∗ , ∗ , and † indicate signiﬁcance at the 1, 5, and 10% levels, respectively.
χ2 indicates Chi-square statistics (p-values in brackets) resulting from tests of the null hypothesis that estimated coeﬃcients on the three monetary policy surprises, P S 1 , P S 2 andP S 3 , are jointly zero for each VAR innovation ut .

Table 4: Forecast Error Variance Decomposition of
Monetary Policy Indicators

Policy Indicator

Monetary BaseComposition

Short-term Rate

Policy Shock

QN

QL

SR

QN

QL

SR

QN

QL

SR

h=0

0.83

0.17

99.00

9.60

16.34

74.06

36.22

53.29

10.49

h = 12

88.03

1.87

10.10

11.01

81.54

7.45

63.54

16.08

20.38

h = 24

94.68

2.97

2.36

28.87

65.36

5.77

54.64

27.88

17.47

h = 36

97.20

1.67

1.13

40.16

54.83

5.01

53.00

30.00

17.00

h = 48

97.70

1.66

0.64

48.06

47.67

4.27

51.97

31.36

16.67

Notes: This table shows the estimated percentage share of the forecast error variance of eachmonetary policy indicator attributable to each monetary policy shock for h months ahead.

52

Table 5: Forecast Error Variance Decomposition of Monetary Policy
Indicators under Alternative Identiﬁcation

Policy Indicator

Monetary BaseComposition

Short-term Rate

Policy Shock

QN

QL

SR

QN

QL

SR

QN

QL

SR

h =0

0.25

98.63

1.11

47.54

21.78

30.68

0

0

100

h =12

57.76

5.39

36.85

34.51

18.63

46.86

42.84

3.68

53.48

h =24

66.76

1.66

31.58

39.33

14.76

45.94

56.87

3.80

39.34

h =36

71.36

1.18

27.46

50.64

11.52

37.84

53.81

9.51

36.68

h =48

74.15

1.22

24.63

56.19

10.17

33.65

46.12

18.85

35.04

Notes: This table shows the estimated percentage share of the forecast error variance of eachmonetary policy indicator attributable to each monetary policy shock for h months ahead.

Table 6: Results for the Regression of Alternative Instruments on
Global Economic Factors

VAR Innovation
Oil
GIP

Monetary Policy Surprise

MB

COMP

SR

-3.49

-1.45

-0.73

(23.74)(1.91)(2.27)

-5.59

∗∗

P S1

P S2

P S3

0.24

-0.07

0.52

(0.62)(0.44)(0.36)

0.00

0.52

-0.04

-0.03

0.07

(3.56)(0.18)(0.39)(0.10)(0.06)(0.04)

∗

∗

-1.16

-0.19

0.26

0.19

-0.17

(8.06)(0.51)(0.79)(0.23)(0.15)(0.11)

USTED

2.10

0.02

1.65

-0.44

0.56

0.16

(7.33)(0.60)(1.22)(0.32)(0.41)(0.12)

FFR

-6.67

-0.53

-0.58

-0.18

-0.43

-0.02

(13.26)(0.75)(0.99)(0.26)(0.25)(0.13)

χ2

8.04

6.18

4.99

4.44

6.67

7.25

[0.15][0.29][0.42][0.49][0.25][0.20]

USEPU

15.93

Notes: This table shows the estimated coeﬃcients in equations (23) and (24). Values inparentheses are robust standard errors. ∗∗ and ∗ indicate signiﬁcance at the 1 and 5%
levels, respectively. χ2 indicates Chi-square statistics (p-values in brackets) resulting fromtests of the null hypothesis that the estimated coeﬃcient on the global economic factors arejointly zero.

53

Figure 1: Size, Unconventional Assets, Call Rate, and Long-term
Bond Yield

Notes: The dark gray and light gray shadows indicate the amounts of unconventional
assets and conventional assets held by the Bank of Japan, respectively. The amounts
are shown in units of 100 trillion Yen on the left-hand scale. Unconventional Assets include Exchange-Traded Funds (ETF), Real Estate Investment Trusts (REIT),
corporate bonds, commercial paper, long-term government bonds, and asset-backed
securities. Conventional assets include other assets such as short-term government
bonds. The plotted and dashed lines indicate the call rate and the 10-year Japanese
government bond yield, respectively. The amounts are shown in units of b.p. (100%)on the right-hand scale.

54

Figure 2: Impulse Responses to the Quantitative Easing, Qualitative
Easing, and Short Rate Shocks

Notes: See Section 3 for details on the identification of each of the monetary policy
shocks. The solid lines represent the point estimates of the impulse responses to aquantitative monetary policy shock in the eight-variable VAR model. The shaded
areas represent the ± one standard error confidence band calculated by the bootstrap
method with 1000 replications. The bootstrap with external instruments involves
resampling from the instruments.

55

Figure 3: Impulse Responses to the Monetary Policy Shocks under
Alternative Identification

Notes: Subsection 5.1 discusses the details of identification of the monetary policy
shocks. Also see the Notes to Figure 2.

56

Figure 4: Impulse Responses to Unanticipated Policy Shocks

Notes: Subsection 5.2 discusses the details of the identification of the unanticipatedmonetary policy shocks. Also see the Notes to Figure 2.

57

Figure 5: Impulse Responses of Uncertainty Indicators

Notes: The solid lines represent the point estimates of the impulse responses of themonetary policy uncertainty indicator (upper row) and VIX (lower row) in Japan. The
impulse responses of the two uncertainty variables are obtained as their responses tothe quantitative and qualitative easing shocks, by including each of the indicators into
the eight-variable VAR model and employing the identification method developed in
Section 3. Also see the Notes to Figure 2.

58

Figure A1: Monetary base and risky asset ratio(a) QE period (2000-2006)(b) QQE period: (2010-2015)

Notes: Comp indicates the ratio of the amount of unconventional assets to the BOJ’s
total assets. MB indicates the monetary base in trillion yen.

59

Figure A2: Estimated monetary policy shocks(a) Quantitative shock (QN)(b) Qualitative shock (QL)(c) Short-term rate shock

Notes: The figure shows estimated monetary policy shocks using monetary policy
surprises.

60