The Effects of QQE on Long-run Inflation Expectationsin Japan∗Mototsugu Shintani†

Naoto Soma‡

This version: October 2020

Abstract
This paper investigates whether a series of unconventional monetary policies conducted by the Bank of Japan in 2013 contributed to an increase in long-run inflation
expectations, which had been below 0 percent. Using a panel dataset of professional
forecasts, we estimate the dynamic Nelson-Siegel model and extract long-run inflation
expectations as a common factor. We find that the introduction of Quantitative andQualitative Monetary Easing (QQE) in April 2013, rather than raising the inflation
target from 1 percent to 2 percent in January 2013, significantly increased long-run inflation expectations in Japan. In addition to this outcome, we find that the correlation
between short-run and long-run expectations has been reduced since the introduction of
QQE. Overall, our results suggest that inflation expectations have been “re-anchored”
to the level around 1 percent since the introduction of QQE, while the level is stillshort of the 2 percent target.
JEL Codes: C53, D84, E31, E37, E52.
Keywords: Anchoring of inflation expectations; Inflation expectations curve; State
space model; Term structure of inflation expectations; Unconventional monetary policy.

∗

We would like to thank Shin-ichi Fukuda, Hibiki Ichiue, Takashi Kano, Eiji Kurozumi, Ryuzo Miyao,
Kazumitsu Nawata, Etsuro Shioji, Shigenori Shiratsuka, Fumiko Takeda, Takayuki Tsuruga, and Kozo Ueda.
The views expressed in this paper are those of the authors and do not reflect those of the Bank of Japan.
†
Faculty of Economics, the University of Tokyo, Japan; and Institute for Monetary and Economic Studies,Bank of Japan. Email: shintani@e.u-tokyo.ac.jp‡
Department of Economics, Yokohama National University. Email: soma-naoto-wb@ynu.ac.jp1

1

Introduction
There is a broad consensus today that the effective management of long-run inflation

expectations is essential for successful monetary policy. However, since the outbreak of the
global financial crisis in 2008, several studies have reported that inflation expectations inmajor countries remain at the historical low levels but are also sensitive to incoming news
or temporary shocks (Galati et al. (2011), Nautz and Strohsal (2015), Buono and Formai(2018), and Corsello et al. (2019)). Such “de-anchoring” of inflation expectations brings aserious concern for central banks, especially when conventional policy tools are constrainedby the effective lower bound. Since low inflation expectations imply high real interest rates,
such unintended monetary tightening makes it difficult to “re-anchor” inflation expectations
to the target level. Given that equilibrium real interest rates are expected to stay at low levels
and the probability of the policy rate hitting the lower bound remains high in the future,
the question of how monetary policy can re-anchor inflation expectations to the target level
without conventional tools becomes an increasingly important issue for central banks aroundthe world.
This paper investigates whether recent monetary policies in Japan, raising inflation target from 1 percent to 2 percent in January 2013 and introducing the unconventional policy
framework called Quantitative and Qualitative Monetary Easing (QQE) in April 2013, contributed to re-anchoring long-run inflation expectations. In comparison with other majorcountries, Japan has been suffering from deflation and low inflation expectations for a much
longer time. Since the end of 1990s, the Bank of Japan (BOJ) has used a variety of unconventional measures to overcome chronic deflation. Of such measures, raising the inflation
target and introducing QQE are especially unique in that they were the first attempt in theworld to re-anchor inflation expectations to a higher level when the policy rate was constrained at the effective lower bound. As the re-anchoring of inflation expectations without2

conventional measures can be a realistic challenge for other central banks, analyzing theJapanese experience will provide some important lessons.
We use ESP Forecast, a panel dataset of professional forecasts about the Japanese economy, to estimate the evolution of long-run inflation expectations. ESP Forecast includes
monthly forecasts of annual inflation reported by around 40 Japanese professionals. Using
this series from 2005 to 2017, we estimate the inflation expectations curves based on the
dynamic Nelson-Siegel model (Nelson and Siegel (1987), Diebold et al. (2006)). Employing
the dynamic Nelson-Siegel model in our analysis offers the following two advantages.
The first advantage is related to the characteristics of our dataset. ESP Forecast collects“fixed-event” forecasts, in which the forecast event is kept fixed in consecutive periods while
the forecast horizon shrinks as time gets closer to the realization of that event. In eachmonth, forecasters report their forecasts of annual inflation rates for the current, next, andfollowing fiscal year until the realized value of target is released to the public.1 In contrast
to the “fixed-horizon” type survey, which collects the forecasts for a constant period (e.g.,
1 year) ahead, the “fixed-event” dataset contains information about inflation expectationsof a wide range of horizons (e.g., 1 month to 35 months in our data). At the same time,
however, there is also a missing observations problem in the sense that forecasts with only
two or three different horizons are reported in each month.2 The dynamic Nelson-Siegel
model has been widely used for fitting the term structure of interest rates, as it provides aflexible way of interpolation and extrapolation to estimate yields for any maturities that are
not directly observable in data. We take advantage of this feature in the context of inflation
expectations and evaluate the term structure of inflation expectations at any horizon and at
any date, even though forecast targets in the survey data are limited to fixed events.
The second advantage is related to our objective. To investigate whether the policy suc1

Many widely used surveys have the same structure, including Consensus Forecasts by Consensus Economics, World Economic Outlook by the International Monetary Fund, and the OECD Economic Outlook.
2
For example, if the forecast date is October, forecasters report only 6, 18, and 30 months ahead forecasts.

3

ceeds in inducing a regime change in the anchoring of long-run inflation expectations, we aim
to capture common variations in inflation expectations among all individual forecasters. By
estimating a panel version of the dynamic Nelson-Siegel model, we can decompose individual
inflation forecasts into fixed effects, three latent factors (level, slope, and curvature), andmeasurement errors. Three latent factors represent the common sources of variations while
the fixed effects and the measurement errors control cross-sectional heterogeneity. Among
three factors, the level factor corresponds to the notion of inflation expectations at the infinite horizon, and we can evaluate the policy effect on the long-run inflation expectations
as a mean shift of the level factor. In terms of adopting the dynamic Nelson-Siegel model
to inflation forecasts from survey data, our approach is similar to that of Aruoba (2020).
However, it should be noted that Aruoba (2020) estimates the dynamic Nelson-Siegel modelusing the time series of aggregated inflation forecasts from several kinds of surveys that havea varied range of forecast horizons. In contrast, we estimate the model using a panel of
non-aggregated individual forecasts from a single survey.
We find that the introduction of QQE in April 2013, rather than raising the inflation
target to 2 percent in January 2013, actually made a significant impact on the expectation
formation process of professional forecasters in Japan. Our estimate suggests that the crosssectional average of long-run inflation expectations increased for 0.50 percent between threemonths before and after the introduction of QQE (from 0.14 percent to 0.64 percent). In
contrast, when we use a same six-month window around the timing of raising the inflation
target, the size of the shift is only 0.17 percent (from 0.11 percent to 0.28 percent). After the
introduction of QQE, long-run inflation expectations, which had previously been around 0
percent, increased to a maximum level at 1.18 percent by the first half of 2014. In mid-2014,
however, long-run expectations began to decline along with the sharp drop in oil prices.
In fact, a series of sizable negative shocks to long-run expectations are observed between

4mid-2014 to early-2016, the period in which oil price continued to fall. Since our estimate
suggests a high degree of intrinsic persistence of long-run expectations in Japan, it takes
time for long-run expectations to move back to the steady state when large negative shocks
occur. On the other hand, our estimate also suggests the reduction of correlation between
short-run and long-run expectations since the introduction of QQE. In previous studies, thelow correlation between short-run and long-run expectations has often been used as evidenceof anchoring. Overall, our result implies that inflation expectations in Japan have been “reanchored” to the level around 1 percent since the introduction of QQE, while the level isstill short of the 2 percent target.
Our finding highlights three possible lessons for central banks, which may confront the
future challenge of “re-anchoring” inflation expectations without relying on the conventional
tools. First, monetary policy can be effective in raising long-run inflation expectations eventhough the short-term policy rate is constrained. With the introduction of QQE in April
2013, the BOJ started to strengthen monetary easing by purchasing a substantial amount of
longer term government bonds, along with other assets such as ETF and J-REIT. In addition,
the BOJ strongly committed to continue such asset purchases until the target of 2 percent
inflation was achieved. The BOJ also stated that QQE would include all the measuresnecessary to achieving the 2 percent target within a time horizon of about two years. Such
substantial and rapid monetary easing, along with aggressive forward guidance succeeded inraising long-run inflation expectations to the higher level, even though the policy rate wasconstrained at the effective lower bound. Second, an announcement of a higher inflation
target itself does not guarantee the re-anchoring of inflation expectations. Both a structural
break test and the estimation of the dynamic Nelson-Siegel model imply that a shift oflong-run inflation expectations occurred in April 2013, not in January 2013. Indeed, inJanuary 2013, the BOJ simply declared that it would “pursue monetary easing to achieve

5the target at the earliest possible time,” without announcing any specific monetary easing
scheme. This fact, combined with our results, suggests that only raising the inflation target
was not sufficient and that specific actions towards the new target were needed to re-anchor
inflation expectations. The lesson may be useful for central banks that consider increasing
their inflation targets in the future. Third, central banks should pay careful attention to
the dynamic properties of long-run inflation expectations and take quick policy actions in
response to a sign of de-anchoring in the case of highly persistent inflation expectations. If
the degree of intrinsic persistence of long-run expectations is high, as is the case in Japan,
negative shocks can induce a deviation from the target for considerable periods of time. In
particular, the reduction of the observed inflation rate caused by some exogenous factors,
such as drops in oil prices, may have long-lasting effects on long-run inflation expectations.
Under such circumstances, the timely implementation of effective policies, such as strong
commitments and unconventional asset purchases, are desirable.
The remainder of the paper is organized as follows. Section 2 briefly reviews a recent experience of monetary policy in Japan and discusses the related literature. Section 3 describes
our dataset and the model. Section 4 reports the empirical results. Section 5 concludes.

2

Background

2.1

Japanese Experience

1990s-2007 (Pre-Crisis Period)
In response to prolonged deflation in the 1990s, the BOJ introduced the zero interest rate
policy in February 1999, which fixed the nominal interest rate at virtually 0 percent. At thesame time, the BOJ explicitly committed to keep it “until deflationary concern is dispelled.”
In March 2001, the BOJ adopted a new unconventional monetary easing framework called
Quantitative Easing, where the main operating target of monetary policy was shifted to the6current account balance at the BOJ. The BOJ committed to continuing this policy “until
the annual rate of change in the consumer price index registers zero percent or above in astable manner.” Although the BOJ implemented such novel monetary policy tools, the mild
deflation and weak growth of the economy continued in the first half of 2000s.
Against the improved economic performance in late 2005, the BOJ decided to exit from
Quantitative Easing and the zero interest rate in March 2006. The standard uncollateralized
overnight rate was reintroduced as the operating target of monetary policy and raised from0 percent to 0.25 percent in July 2006, and further, to 0.5 percent in February 2007. At thesame time, to guide inflation expectations of private agents in Japan, the BOJ started toreport the level of the inflation rate, which was recognized as price stability by each Policy
Board member, in an annual survey called “understanding of medium to long-term pricestability.” From 2006 to 2011, the reported level varied across the Policy Board members
but the midpoint of the range was always about 1 percent. Therefore, one may want tointerpret 1 percent as the BOJ’s implicit inflation objective during this period. However,the BOJ at that time repeatedly stressed that it was neither an inflation target nor inflation
objective.
2008-2012 (Post-Crisis Period)
After the global financial crisis in 2008, exports to overseas economies declined and Japanencountered a severe recession in the deflationary regime. While the BOJ immediately
reduced the policy interest rate in response to the collapse of Lehman Brothers in the fall of
2008, Japan’s room for further monetary easing in the conventional way was limited. Suchan environment led the BOJ to introduce the Comprehensive Monetary Easing policy in
October 2010. In particular, the policy rate was set to zero again and the BOJ committed tomaintaining the zero interest rate “until it judges that price stability is in sight on the basisof the understanding of medium to long-term price stability.” In addition to this measure,7

the BOJ tried to reduce longer-term interest rates instead of constrained short-term policy
rates by purchasing various financial assets, such as Japanese government bonds, corporate
bonds, and ETF.
In February 2012, the BOJ introduced for the first time the explicit inflation target of
1 percent. In particular, it replaced the previous “understanding of medium to long-term
price stability” with a “price stability goal in the medium to long-term.” While the former
simply reported the range of views about a desirable inflation rate held by Policy Board
members, the latter was set as a consensus among all members of the Policy Board. The“price stability goal” was set to “a positive range of 2 percent with a midpoint of around 1
percent for the time being,” but also would be reviewed once a year.
Although the BOJ tried to maintain accommodative financial conditions through theasset purchase and forward guidance, the CPI inflation rate in Japan remained either negative
or fluctuated at around 0 percent at the end of 2012. Against this background, the BOJ
decided to raise the inflation target from 1 percent to 2 percent in January 2013. In theannouncement, the BOJ declared that it would pursue monetary easing and aim to achieve
the target at the earliest possible time. On the other hand, it refrained from introducing a
specific monetary easing program at that point in time.
2013-present (QQE Period)
As a concrete means of achieving the inflation target of 2 percent at the earliest possible
time, the BOJ introduced a new unconventional monetary policy framework called QQEin April 2013. In the introduction of QQE, the main operating target of monetary policy
was shifted from the policy rate to the monetary base. The BOJ promised to increase themonetary base at an annual pace of about 60 to 70 trillion yen, and the amount of Japanese
government bonds purchased were substantially increased, compared to the period of Comprehensive Monetary Easing. In addition to such “quantitative” accumulations, the BOJ
8also tried to utilize a “qualitative” device. In particular, the BOJ extended the averageremaining maturity of bond purchases from about three years to seven years by preferentially purchasing long-term bonds with maturities up to 40 years. Furthermore, the BOJ
introduced the forward guidance policy regarding asset purchases so that the QQE would
be continued as long as the BOJ would deem it necessary for maintaining the price stability
target in a stable manner. The BOJ also declared that QQE would include all the necessary
measures to achieve the target of 2 percent within a time horizon of about two years.
Since the 1990s, Japan had almost never experienced as much as 2 percent inflation. At
the time QQE was introduced, Japan had been suffering from prolonged deflation for morethan a decade. In this respect, the challenge of QQE was truly “unprecedented (Kuroda(2015)).” The main concern of the BOJ has been the possibility that the past experienceof chronic deflation inclines households and firms to form strong deflationary expectations,
reduce consumption and investment, and cause further deflation in a self-fulfilling manner.
Therefore, the aim of QQE was “to dispel the deflationary mindset and raise inflation expectations (Kuroda (2015)).” Compared to Quantitative Easing in 2001 and Comprehensive
Monetary Easing in 2010, QQE in 2013 was distinctive in the sense that the anchoringof long-run inflation expectations was explicitly treated as a central objective and a key
transmission mechanism of the policy.
In response to a sharp drop in oil prices that caused a decline in inflation in mid-2014, theBOJ decided to expand QQE in October 2014. The annual pace of increase in the monetarybase was accelerated and the average remaining maturity of government bond purchases was
extended to about 7 to 10 years. Nevertheless, inflation rates remained significantly below
the target in 2015, partly due to a further decline in oil prices. In January 2016, the BOJ
introduced QQE with a Negative Interest Rate policy by adopting a −0.1 percent interest
rate on part of the excess reserves. At the same time, the average remaining maturity of9

government bond purchases was further extended to 7 to 12 years. In September 2016, theBOJ introduced QQE with Yield Curve Control, which mainly consists of two parts. The
first part is called the “yield curve control,” which aims to control long-term interest rates
through market operations. The second part is called “inflation-overshooting commitment”
in which the BOJ commits itself to expanding the monetary base until the inflation rate
exceeds the 2 percent target and stays above the target in a stable manner. It should be
noted that, while several variations of QQE had been introduced over the past few years,
the anchoring of long-run inflation expectations remained a central objective of QQE.
In summary, recent monetary policies in Japan are noteworthy in that it used unconventional tools to raise inflation expectations from a level substantially below the target.3
We believe that analyzing the effects of such a policy will provide some important lessons toother central banks.

2.2

Related Literature

Time Series Analysis of Inflation Expectations Survey Data
Our paper is related to the previous studies that adopt the dynamic Nelson-Siegel model
to inflation expectations. The paper most similar to ours is Aruoba (2020), who estimates theterm structures of inflation expectations and real interest rates in the United States. Lewis
and McDermott (2016) also employ a similar approach to Aruoba (2020) and examine theeffect of the past changes in the inflation target by the Reserve Bank of New Zealand. There
are also other studies that estimate the term structure of inflation expectations from survey
data but with different methodologies. Kozicki and Tinsley (2012) propose the shiftingendpoint autoregressive (AR) model to jointly explain inflation and inflation expectations.
Winkelried (2017) modifies the shifting-endpoint AR model to fit the fixed-event forecast
3
About the difficulties of anchoring inflation expectations when the central banks are targeting inflationfrom below, see Ehrmann (2015).

10

data. A similar strategy is also taken by Mehrotra and Yetman (2018). These studies
explicitly assume that inflation evolves according to an AR process and inflation expectations
are formed as efficient and unbiased predictors. Inflation forecasts are assumed to eventuallyconverge to the point called the “shifting-endpoint” (in Kozicki and Tinsley (2012)) or the“anchor” (in Mehrotra and Yetman (2018)) as the forecast horizon increases. It should be
noted that these concepts correspond to the level factor in the dynamic Nelson-Siegel model.
Since our objective is to extract common variations in inflation expectations of individual
forecasters, we do not need to specify how forecasters form their expectations nor what
information set they use.
Anchoring of Inflation Expectations
There is a growing literature examining the anchoring of inflation expectations. Gürkaynak
et al. (2010) compare long-run inflation expectations in several countries by using the difference between forward rates on nominal and inflation-indexed bonds. Beechey et al. (2011)combine financial market data and survey data to examine the anchoring of inflation expectations in the euro area and the United States. Galati et al. (2011) also use financial market
data to investigate whether the global financial crisis in 2008 affected inflation expectationsin the United States, the euro area, and the United Kingdom. Ehrmann (2015) uses survey
data in the countries adopting inflation targeting and argues that de-anchoring of inflation
expectations is likely to occur under persistently low inflation compared to situations where
inflation is around the target. Autrup and Grothe (2014) find that the degree of anchoringof inflation expectations in the United States decreased by the outbreak of the crisis. In
addition, Nautz and Strohsal (2015) report that expectations in the United States have notbeen re-anchored ever since. Buono and Formai (2018) find that inflation expectations havebeen firmly anchored at the target in the United States but de-anchored in the euro area andJapan. Grishchenko et al. (2019) and Corsello et al. (2019) also report that de-anchoring11

has occurred in the euro area.
Empirical Analysis of QQE
Several researchers recently examined the effect of QQE on inflation expectations inJapan. Using survey data of Japanese households, Nishiguchi et al. (2014) report that morehouseholds have forecasted price increases than price declines and that the distribution of
their expectations has shifted in the inflationary direction after the introduction of QQE.
Kamada et al. (2015) use the same dataset as Nishiguchi et al. (2014) and conclude that QQE
actually strengthened the anchor of long-term inflation expectations. In contrast, Nakazono(2016) argues that recent monetary policies in Japan may have not been sufficient to change
the private sector’s outlook for future inflation, according to various surveys for households,
firms, professionals and market participants. Hattori and Yetman (2017) adopt the model of
Mehrotra and Yetman (2018) to Consensus Forecasts data from Consensus Economics andreport that long-run inflation expectations in Japan fell from the mid-1990s but have risen inrecent years. Hogen and Okuma (2018) estimate a model of expectations formation proposedby Carvalho et al. (2017) and conclude that inflation expectations in Japan temporarily
increased after QQE but have not yet been anchored to the 2 percent target. Maruyama
and Suganuma (2020) combine various forecast data and estimate a system of equations that
include the term structure of inflation expectations as a part of the system. They conclude
that short-term inflation expectations increased since the introduction of QQE. Last but notleast, in September 2016, the BOJ itself conducted the official assessment of QQE over the
first three years following the introduction and reported it as a “Comprehensive Assessment”(Bank of Japan (2016a,b)). Our paper complements these papers by estimating long-run
inflation expectations as common variations in individual inflation forecasts reported byprofessional forecasters.

12

3

Data and Model

3.1

Data

ESP Forecast is a monthly survey of the macroeconomic forecasts, which has been conducted for more than fifteen years in Japan. One main advantage of ESP Forecast for ouranalysis is that all the participants are leading forecasters from private institutions in Japan,
who are familiar with the Japanese economic environment and have regularly been involvedin the Japanese financial market. Each month, about 40 forecasters are asked to provide
their personal forecasts for macroeconomic and financial variables in Japan over the current,
next, and following fiscal years.4 The survey is conducted from the last week of a particularmonth to the first week in the next month. About a week after the collection of the survey,
a formal monthly report is published by the Japan Center for Economic Research.
In our analysis, we use individual forecasts of annual inflation rates for the current, next,
and following fiscal years from ESP Forecast. Inflation rates are based on the changes in
the consumer price index of all items, less fresh foods. We construct a balanced panel of
28 forecasters for the sample period from April 2005 to March 2017. Figure 1 shows the
distribution of individual inflation forecasts for four selected target fiscal years, 2007, 2010,2013 and 2016. The horizontal axis denotes the forecast horizons. Dashed line and solidline represent realized values and median forecasts, respectively. The dots in the figure fora particular forecast horizon represent the individual inflation forecasts made at the sametiming. Therefore, the range of dots for a given forecast horizon corresponds to a measureof disagreement. As the forecast horizon becomes shorter, disagreement among individual
forecasts declines. Since ESP Forecast collects the “fixed-event” type of forecasts, the forecast
event is kept fixed while the forecast horizon shrinks as time gets closer to the final monthof the target fiscal year. The figure shows that, because of increasing information, their
4

In Japan, the fiscal year is from April 1 to March 31.

13forecasts eventually converge to the realized inflation rate.
Our intention is to investigate the effects of raising the inflation target to 2 percent inJanuary 2013 and the introduction of QQE in April 2013 on long-run inflation expectations.
As a preliminary analysis, we first examine whether these policies actually result in somechanges in the inflation forecasts series. In particular, we apply multiple structural breaktests proposed by Bai and Perron (1998, 2003) to the time series of the cross-sectional averageof individual inflation forecasts. To construct a single time series, we pool the forecasts forthe current, next, and following target fiscal years. Following the advice of Bai and Perron(2003), we first look to see if at least one break is present using the double maximum
statistics, and then determine the number of breaks using the sequential procedure based on
the sup F statistics.
Table 1 presents the results of the structure break test. For the double maximum statistics
of Bai and Perron (1998), allowing for up to 5 breaks with a trimming fraction of 0.15, both
UDmax (= 451.78) and WDmax (= 775.99) are above the corresponding critical values at the
1 percent significance level. Thus, the null hypothesis of no structural break is significantly
rejected. The sup F test statistic for the null of no break against an alternative of 1 break(F (1|0)) and for the null of 1 break against an alternative of 2 breaks (F (2|1)) is 155.01
and 309.48, respectively. Again, at the 1 percent significance level, both the null hypothesesof no break and 1 break are rejected. In contrast, the sup F test statistic for the null of 2
breaks against an alternative of 3 breaks (F (3|2)) is 10.08 and less than the critical values at
the 1 percent significance level. To summarize the results in Table 1, we can conclude that
there are 2 breaks in the series of average inflation forecasts. Table 2 reports the estimateddates for 2 structural breaks and their 95 percent confidence intervals. The first break is in
November 2008, which is soon after the bankruptcy of Lehman Brothers. The second breakis in April 2013, which matches the timing when QQE was introduced by the BOJ. The latter14

result suggests that the regime change in inflation expectations formation in Japan occurredat the timing of the introduction of QQE and not at the timing of raising the inflation
target in January 2013. In the table, the subsample means of the average inflation forecasts
for three estimated regimes are also reported. The number suggests that the mean of theaverage inflation forecasts increased from -0.59 percent to 0.76 percent after the introductionof QQE.
Table 1: Bai and Perron (1998) Test for Multiple Structural Breaks in the Mean
UDmax

WDmax (1%)

F (1|0)F (2|1)

F (3|2)451.78(7.46)

775.99(13.83)155.01(12.29)309.48(13.89)10.08(14.8)

Notes : The critical values of test statistics at the 1 percent
significance level are reported in parentheses.

Table 2: Bai and Perron (2003) Estimates of Mean Inflation Forecasts
Regime 1

Regime 20.46 (0.055)
2008:11 [2008:06,2009:02]

Regime 3−0.59 (0.049)
0.76 (0.060)
2013:04 [2013:03,2013:07]

Notes : The first row reports the estimated mean of inflation forecasts
for each regime with the standard error in parentheses. The second row
reports the end date for each regime with the 95% confidence interval inbrackets.

3.2

Dynamic Nelson-Siegel Model

We denote the annual inflation rate of the target fiscal year by
ΠT =πT −11 + · · · + πT
,
12

15

(1)

where πt is the year-on-year monthly inflation rate at the month t and the uppercase letter T
denotes the final month (March) of a target fiscal year. Given that the sample period is from
April 2005 to March 2017, there are total of 12 target fiscal years and we can write T = 12j
for j = 1, · · · , 12. Suppose that we now are at the period t = T − h, h months before thefinal month T of a particular target year (t = 1, · · · , 144, 1 ≤ h ≤ 35). ESP Forecast thencollects the forecast for (1), which we denote as
ΠT |t=T −h =πT −11|T −h + · · · + πT |T −h
,
12

(2)

where πs|t is the forecast for the monthly inflation at the period s made at the period t.
It should be noted that since πs|t = πs for s ≤ t, (2) contains realized values of monthly
inflation when h ≤ 11.
Our objective is to extract the complete term structure of inflation expectations byusing the observations (2). In particular, we need to compute the τ -month average inflation
expectations from month t + 1 to t + τ denoted asπ̄t (τ ) =πt+1|t + · · · + πt+τ |tτ

(3)

as a function of τ . If (3) is directly observable for any length τ at any period t in thedataset, we can immediately achieve our objective. However, as in most other surveys, thereported inflation forecasts in ESP Forecast do not correspond exactly to (3). As we cansee by comparing (2) and (3), the observed forecast ΠT |t=T −h coincides the estimation targetπ̄t (τ ) only when the forecast date t is exactly 12 months before T . At only this timing, theobservable (2) becomesΠT |t=T −12 =πT −11|T −12 + · · · + πT |T −121216and coincides withπ̄t (12) =πt+1|t + · · · + πt+12|t
12

as t = T − 12. For almost all timings other than h = 12, we cannot directly observe
the estimation target (3). To use all the observations and extract as much information as
possible, we need a model to connect the observable (2) and the estimation target (3). To thisend, we take a similar approach to that of Aruoba (2020) and use the dynamic Nelson-Siegel
model. In particular, we assume that π̄t (τ ) is generated from the process given byπ̄t (τ ) = Lt −1 − e−λτ
λτ




St +

1 − e−λτ− e−λτ
λτ


Ct ,

(4)

where Lt , St , and Ct are time-varying level, slope, and curvature factors.
In the specification above, the level factor Lt can be expressed as
Lt = lim π̄t (τ ).
τ →∞

(5)

Thus, this level factor measures the long-run inflation expectations held by individual forecasters at time t. It also corresponds to the notion of the “shifting-endpoint” in Kozicki andTinsley (2012) and that of the “anchor” in Mehrotra and Yetman (2018).
On the other hand, the slope factor St captures the difference between long-run andshort-run inflation expectations, and the curvature factor Ct captures whether medium-run
expectations are higher or lower than short-run expectations and long-run expectations. The
parameter λ determines the effect of each factor on π̄t (τ ) through factor loadings.

17

When h > 11 (i.e., t < T − 11), we use the definition (3) to rewrite (2) as
πT −h+1|T −h + · · · + πT |T −h h − 12 πT −h+1|T −h + · · · + πT −12|T −h
h
×
−
×
12
h
12
h − 12h − 12hπ̄T −h (h − 12)(6)= π̄T −h (h) −1212hh − 12= π̄t (h) −π̄t (h − 12),12
12

ΠT |t=T −h =where the last equality uses the fact that t = T − h. By substituting the model equation (4)to (6), we obtain −λ(h−12)1 e−λ(h−12) − e−λhe
− e−λh−λ(h−12)−λh+ (h − 12)e
− he
St +
Ct .
ΠT |t=T −h = Lt +
12h
12
λ
(7)
When h ≤ 11 (i.e., t ≥ T − 11), the forecast date t is within one year before the final monthof the target fiscal year. Using the fact that
πT −11|T −h = πT −11 , · · · , πT −h|T −h = πT −h ,hold for h ≤ 11, (2) can be decomposed as
πT −11 + · · · + πT −h + πT −h+1|T −h + · · · + πT |T −h12
πT −11 + · · · + πT −hh=+ π̄T −h (h)1212πt+h−11 + · · · + πth=
+ π̄t (h).
1212

ΠT |t=T −h =(8)

By substituting the model equation (4) to (8), we obtain
12
h







πt+h−11 + · · · + πt
1 − e−λh1 − e−λh−λh
ΠT |t=T −h −= Lt −St +−e
Ct . (9)
12
λh
λh

18

Based on (7) and (9), we set the measurement equations as −λ(h−12) −λh  −λ(h−12) −λh

i,ESP
e
−e
1
e
−e
−λ(h−12)−λhΠT |t = ci + Lt +St + 12
Ct + εit,h ,+ (h − 12)e
− he

12λ
λ






(if t < T − 11)













12
h



Πi,ESP− πt+h−1112+···+πt
T |t= ci + L t −

1−e−λh
λh



St +



1−e−λh− e−λhλhCt + εit,h ,(if t ≥ T − 11)(10)where Πi,ESP
denotes the forecast corresponding to (2) reported by the forecaster i at the
T |tperiod t in ESP Forecast. We introduce an idiosyncratic fixed effect ci , which captures thetime-invariant heterogeneity of inflation expectations among individual forecasters. On theother hand, Lt , St , and Ct capture the dynamics of unobserved factors, which induce common
variations in inflation expectations. All deviations of the observed inflation forecasts fromthe model predictions are captured by the measurement error εit,τ ∼ N (0, στ2 ).
The three latent factors are assumed to follow the independent AR processes of order pgiven by
Lt = µL + ρ11 (Lt−1 − µL ) + · · · + ρ1p (Lt−p − µL ) + ηtL ,St = µS + ρ21 (St−1 − µS ) + · · · + ρ2p (St−p − µS ) + ηtS ,(11)

Ct = µC + ρ31 (Ct−1 − µC ) + · · · + ρ3p (Ct−p − µC ) + ηtC ,kwhere ηtj ∼ N (0, σj2 ) and Cov(ηtj , ηt−s
) = 0 for j, k = L, S, and C, and s ≥ 0. The lag-orderp is chosen by minimizing information criteria. We also consider the vector autoregressive(VAR) specification as an alternative to (11). Given the result of the structural break
test, we allow the long-run average of the level factor µL to change over the three periods:
before November 2008, from November 2008 to March 2013, and after April 2013, whichcorresponds to the timing of the introduction of QQE. We also consider the case when April

19

2013 is replaced by January 2013, which corresponds to the timing of raising the inflation
target from 1 percent to 2 percent.
In the next section, a state space model, which consists of the measurement equations (10)and the transition equations (11), is estimated by using maximum likelihood combined with
the Kalman filter. While there are missing observations in our dataset due to the structure of
the survey, the Kalman filter and the state space model are well suited to handle them. The
three factors, Lt , St , and Ct , are estimated using the Kalman smoother. A total of 71 + 3p
2parameters are estimated, where 35 of them are measurement error variances σ12 , · · · , σ35.

4Estimation Results
Table 3 reports the AIC and BIC for the model with AR(1), AR(2), AR(3), and VAR(1)transition equations. The upper panel shows the results under the assumption that thesecond mean shift occurred in January 2013, which corresponds to the timing of raising the
inflation target. The lower panel shows the results under the assumption that the second
mean shift occurred in April 2013, which corresponds to the timing of the introduction of
QQE. As the table suggests, the AR(2) model with the mean shift in April 2013 is thepreferred specification based both on AIC and BIC.5
Panel (a) of Table 4 shows the estimated parameters of the selected specification, which
we call the benchmark model. The level factor Lt has a mean of 0.56 percent before November
2008 (µL,1 in Panel (a) of Table 4), −0.42 percent from November 2008 to March 2013 (µL,2 ),and 0.87 percent after April 2013 (µL,3 ).6 The sum of the AR coefficients of the level factor
is 0.92 (= 1.44 + (−0.52)), suggesting that long-run inflation expectations are intrinsically5

We also estimate a version of the model which allows the contemporaneous correlation among AR
disturbances of the three factors using the non-diagonal covariance matrix. All the estimates of the nondiagonal terms of the covariance matrix are insignificant and the estimates of the other parameters are almost
the same as the benchmark model.
P28
6
For identification of the mean parameter of the level factor, we normalize the sum of fixed effects i=1 ci
to be 0. The model without fixed effects ci ’s is not selected since AIC = 948.95 and BIC = 1097.44.

20

Table 3: Model Selection Criteria
Model
AR(1)

AR(2)

AR(3)

VAR(1)

January 2013 (inflation target change)
AIC
BIC

368.70588.37343.01571.69651.75887.80659.18896.65

April 2013 (introduction of QQE)
AIC
BIC

358.00577.76331.79560.47642.36879.94647.21884.80Notes : AR(1), AR(2), and AR(3) specifications impose theindependence of error terms of three factors.

persistent. The slope factor St , which represents the difference between long-run and shortrun inflation expectations, has a mean of 0.32 percent. The curvature factor Ct has a meanof 0.98 percent, which suggests that medium-run expectations are typically higher thanshort-run expectations and long-run expectations, and that the inflation expectations curveis mildly inverse-U shaped on average.
Panel (b) of Table 4 shows the estimated parameters of the measurement equations. The
estimated value of parameter λ is 0.060, suggesting that the loading on the curvature factor
is maximized at a forecast horizon of 52 months. Panel (b) also contains the estimatedvariances of the measurement errors. On the whole, the variances are relatively small, given
the unit of dependent variable, namely the annual inflation expectations.
Figure 2 presents the smoothed estimates of the three latent factors. The level factor
sharply drops from over 1 percent to the negative area soon after the global financial crisis in
2008. It recovers to be slightly positive over the next two periods, but still fluctuates around
0 percent until 2013. After the introduction of QQE, it steadily increases and reaches about
1.18 percent at the highest. The slope and curvature factors are positive for most of theperiods. The slope declines soon after the introduction of QQE but starts to increase at the
beginning of 2014. It implies that the difference between long-run and short-run inflation
21

Table 4: Estimation Results: Benchmark Model(a) Transition equation

Level
ρ11
ρ21
µL,1
µL,2
µL,3
σL2

Slope

1.44
(0.19)−0.52(0.17)

ρ21

0.56(0.26)−0.42(0.27)
0.87(0.38)
0.009(0.002)

Curvature

0.88(0.17)
0.004(0.17)

ρ31

µS

0.32
(0.18)µC

0.98(0.90)

σS2

0.04(0.01)

σC2

0.073(0.072)

ρ22

ρ32

1.51
(0.39)−0.59(0.36)(b) Measurement equation

σ12
σ22
σ32
σ42
σ52
σ62
σ72
σ82
σ92
2
σ10
2
σ11
2
σ12

0.012
(0.0018)0.010(0.0011)0.015(0.0018)
0.009(0.0009)
0.015(0.0018)
0.023(0.0029)
0.013(0.0014)0.040(0.0031)0.066(0.0063)0.071(0.0067)
0.092(0.0063)
0.033(0.0039)λ

0.060(0.011)

2
σ13

0.041
(0.0030)
0.040(0.0036)0.047(0.0057)
0.058(0.0098)
0.063(0.0086)
0.073(0.0120)0.059(0.0096)0.057(0.012)0.066(0.019)0.093(0.022)0.132(0.0071)
0.059(0.0080)

2
σ14
2
σ15
2
σ16
2
σ17
2
σ18
2
σ19
2
σ20
2
σ21
2
σ22
2
σ23
2
σ24

2
σ25
2
σ26
2
σ27
2
σ28
2
σ29
2
σ30
2
σ31
2
σ32
2
σ33
2
σ34
2
σ35

Notes : Standard errors in parentheses.

22

0.054
(0.0087)
0.077(0.0081)
0.073(0.020)0.087(0.012)0.118(0.016)0.085(0.010)0.113(0.018)0.123(0.015)0.099(0.016)0.085(0.026)0.145(0.013)

expectations starts to widen during this period, which roughly coincides with the start of the
sharp drop in oil prices. In the later subsection, we will discuss this point in greater detail.
Inflation Expectations Curve and Long-run Inflation Expectations
Once we estimate all the parameters and latent factors, we can draw the inflation expectations curve, which is useful to see the term structure of inflation expectations at thegiven period. Figure 3 compares the estimated inflation expectations curves at three months
before and after the introduction of QQE in April 2013. In the figure, each solid line represents the inflation expectation held by each individual forecaster. As pointed out in theprevious subsection, medium-run expectations are higher than short-run and long-horizon
expectations in both periods, which leads to mild inverse-U shaped inflation expectations
curves. The figure shows that inflation expectations at all horizons increase at the same timeafter the introduction of QQE. On the other hand, Figure 4 shows the result of the similar
comparison at the timing of raising the inflation target in January 2013. As the figure shows,there is little change in long-run inflation expectations, while short-run inflation expectations
decline a bit rather than increase.
Let us now turn to the evolution of the long-run expectations, the level to which theindividual forecaster believes that future inflation will eventually converge as the forecasthorizon increases to infinity. Figure 5 shows the estimated individual long-run inflation
expectations, ci + Lt . As in Figure 3, each solid line represents the inflation expectation
held by each individual forecaster. The result here implies that the introduction of QQEin April 2013, rather than raising the inflation target in January 2013, actually made asignificant impact on the expectation formation process of professional forecasters in Japan.
In comparing the timing of three months before and after the introduction of QQE, we seethat the cross-sectional average of estimated long-run inflation expectations changes from
0.14 percent to 0.64 percent. In contrast, when a similar comparison is made at the timing of23

raising the inflation target, the change is much smaller (from 0.11 percent to 0.28 percent).
The dotted line in Figure 5 plots the realized monthly year-on-year inflation rate (allitems, less fresh foods). While the change in the long-run expectations is modest comparedto that of the actual inflation, they basically move closely at least until the introduction of
QQE. In response to the surge in the short-term realized inflation around the first half of2008, inflation expectations reached the highest level of 1.5 percent. However, the rise in
expectations was completely wiped out by the occurrence of Lehman shock and the subsequent global financial crisis. Afterwards, both realized inflation and inflation expectations
remained negative for awhile. Although the BOJ first introduced the formal inflation target of 1 percent in February 2012 and raised it to 2 percent in January 2013, the monthly
inflation rate and long-run expectations fluctuated around 0 percent during this period.
Immediately after the introduction of QQE, both the realized monthly inflation rate andlong-run inflation expectations rose sharply. The rise in inflation expectations remained
steady at least until the first half of 2014. However, soon after the drop in oil prices in
mid-2014, the growth slowed down and monthly inflation also fell significantly at the sametime. After 2015, the long-run inflation expectations finally started declining and fell further
in January 2016.
In the “Comprehensive Assessment” (Bank of Japan (2016a,b)), the BOJ argues that asa result of QQE, “economic activity and price developments improved, and Japan’s economy
is no longer in deflation.” However, at the same time, the BOJ acknowledges that “the pricestability target of 2 percent has not been achieved” and argues that it is “largely due todevelopments in inflation expectations.” The BOJ points out that “expectations formationin Japan is largely adaptive, that is, backward-looking” in the sense that it is strongly
influenced by the course of the past inflation rate. In summary, the BOJ takes the viewthat the combination of the adaptive expectation formations of Japanese private agents and24decline in the observed inflation rate due to exogenous factors, including the sharp decline
in oil prices since mid-2014, resulted in weakened inflation expectations.7
In our analysis, the estimated dynamics of long-run inflation expectations can be used
to support the BOJ’s explanations in the “Comprehensive Assessment.” Figure 6 showsthe estimated time series of the exogenous shocks to the level factor (ηtL in (11)) after the
introduction of QQE. The shaded area shows the periods of oil price decline.8 Large negative
shocks to the level factor occurred in January 2015, May 2015, and February 2016. Since thelong-run inflation expectations in Japan are intrinsically persistent, these negative exogenous
shocks had long-lasting effects. One interpretation would be, as the BOJ explained in the“Comprehensive Assessment,” that the sharp instances of decline in the observed inflation
rates become negative exogenous shocks to the long-run inflation expectations of individual
forecasters.
Anchoring of Inflation Expectations
It is known that the correlation between short-run and long-run inflation expectations canbe an indicator for the degree of inflation expectations anchoring.9 Suppose that now there isa temporal shock to the current inflation rate. If inflation expectations are well-anchored andforecasters expect such shocks to disappear soon, only the short-run expectations should be
affected, and medium- to long-run expectations should change only slightly. In the extreme
case, if the forecasters believe that future inflation will eventually converge to the central
bank’s inflation target no matter what happens, fluctuations in short-run expectations should
be completely unrelated to long-run expectations. On the contrary, if inflation expectations
are “de-anchored,” the exogenous shock induces fluctuations in medium to long-run expec7

As to the other exogenous factors to have possibly affected inflation expectations in these periods, theBOJ refers to the weakness in demand following the consumption tax hike in April 2014 and the slowdown
in emerging economies since mid-2015.
8
Following Kilian and Baumeister (2016), we set the period from June 2014 to March 2016.
9
For the comprehensive discussion of the method to assess the anchoring of inflation expectations, see
Kumar et al. (2015) and Bems et al. (2018).

25

tations, and even the expectations of the distant horizon should change significantly in thesame direction as short-run expectations. In that sense, comovements between short-run
and long-run inflation expectations can be one of the indicators for the degree of inflation
expectations anchoring.
Figure 7 shows the time series of the cross-sectional mean of the estimated inflation expectations at several selected horizons: 1-month, 1-year, 2-year, and 5-year. The eyeballanalysis of the figure suggests that the correlation between short-run and long-run inflation expectations seems to become weaker in the third regime than in the second regime.
Thus, we can conjecture that the anchoring of inflation expectations became stronger in thethird regime. In what follows, we formally investigate the degree of anchoring by using theextended version of the dynamic Nelson-Siegel model.
We consider a simple extension of the benchmark model to allow not only the mean µL
but also the conditional volatility of the level factor σL2 to vary over the three regimes. The
result is summarized in Table 5.10 The estimated mean and persistence of each factor arealmost the same as the benchmark case. As a result, the evolution of the estimated long-run
inflation expectations is almost the same as shown in Figure 8. The conditional volatility
2
in Panel (a)), 0.021 in the second regimeof the level factor is 0.005 in the first regime (σL,1
2
2
).
(σL,2
), and 0.002 in the third regime (σL,3Allowing the shift in the conditional volatility of the level factor in the extended model
provides us with two additional implications on the properties of long-run inflation expectations. First, the result suggests that the uncertainty of long-run inflation expectations
declines after the introduction of QQE. While monitoring whether the level of inflation expectations is in line with the inflation target is the widely used indicator of the degree ofanchoring, policymakers may also pay attention to the uncertainty of long-run inflation expectations. As Grishchenko et al. (2019) argue, the level of long-run inflation expectations
10

For brevity, we omit the estimated variances of the measurement errors from the table.

26can remain stable at some point, even if uncertainty of expectations is relatively high. Whenthe uncertainty of inflation expectations is higher, agents in the economy expect higher
upside or downside risks to future inflation, leading to a higher probability that inflation
expectations will be de-anchored when exogenous shocks occur in the future. Our estimatessuggest that such a probability has been reduced with the introduction of QQE.
Table 5: Estimation Results: Extended Model(a) Transition equation

Level
ρ11
ρ21
µL,1
µL,2
µL,3
2
σL,1
2
σL,2
2
σL,3

Slope

1.52
(0.20)−0.59(0.19)

ρ21

0.50(0.24)−0.42(0.75)
0.87(0.19)
0.005(0.003)0.021(0.006)0.002(0.001)

Curvature

0.86(0.19)
0.022(0.18)

ρ31

µS

0.35
(0.20)µC

1.01(0.90)

σS2

0.05(0.01)

σC2

0.10(0.099)

ρ22

ρ32

1.37
(0.46)−0.42(0.44)(b) Measurement equation

λ

0.060
(0.009)Notes : Standard errors inparentheses.

Second, as we previously mentioned, the correlation between short-run and long-run
inflation expectations can be an important indicator for the degree of inflation expectations
anchoring. To investigate the anchoring of inflation expectations, several studies (Kumar
et al. (2015), Strohsal et al. (2016), Buono and Formai (2018) and Yetman (2020)) utilize

27the following type of regressionπ̄t (τ ) = ατ + βτ πt+1|t + eτ t .

(12)

In this regression, the smaller βτ implies the lower path-through from short-run expectations
to long-run expectations and thus the higher degree of anchoring. Kumar et al. (2015) referto such a regression analysis as the test of “increasingly anchored” inflation expectations.11
In our model, the coefficient βτ is related to the variance of the three factors. Indeed, βτ inour model can be calculated as
Cov(π̄t (τ ), πt+1|t )V ar(πt+1|t ) −λτ
  −λ −λτ   −λ 
1−e
1−e
1−e
−λτ
−λ
V
ar(S
)
+
V ar(Ct )V ar(Lt ) + 1−eλτ
−
e
−
e
t
λ
λτ
λ
,
=
2

2

−λ
−λ
V ar(Lt ) + 1−eλV ar(St ) + 1−eλ − e−λ V ar(Ct )

βτ =

(13)
where we use the fact that the factors are assumed to be mutually independent. As can beseen from the above calculation, the decline in the conditional volatility of the level factor in
our specification directly leads to the decline in the coefficient βτ for the horizon τ > 1. With
the estimation results in Table 5, Figure 9 plots βτ as a function of the forecast horizon τ inthe period from November 2008 to March 2013 (the dotted line) and after April 2013 (thesolid line), respectively. When the horizon τ increases, the factor loadings on the slope andcurvature become smaller and thus βτ becomes smaller. As we have conjectured, βτ becomes
smaller after the introduction of QQE, which implies that the correlation between shortrun and long-run expectations becomes smaller and the anchoring of inflation expectations
becomes stronger.1211Strictly speaking, they propose the several mathematical conditions for characterizing the degree of
inflation expectations anchoring and discuss the relations between such conditions and the regression basedon (12). See Kumar et al. (2015) for details.
12
For robustness, we also estimate the version of the model that allows (i) the AR coefficients of the all
factors to vary over the three groups of the periods, and (ii) the variance of the slope and curvature factors

28

5Conclusion
This paper investigates whether a series of unconventional monetary policies conductedby the Bank of Japan in 2013 contributed to increasing long-run inflation expectations, which
had been below 0 percent. Using a panel survey data of Japanese professional forecasters,
we estimate the dynamic Nelson-Siegel model and inflation expectations curves over various
forecast horizons.
We find that the introduction of Quantitative and Qualitative Monetary Easing (QQE)in April 2013, rather than raising the inflation target from 1 percent to 2 percent in January
2013, actually made a significant impact on the expectation formation process of professional forecasters in Japan. After the introduction of QQE, long-run inflation expectations
increased to a maximum of 1.18 percent by the first half of 2014 but declined afterward. Ourestimates suggest a high degree of intrinsic persistence of long-run expectations in Japan,
in the sense that it takes time for long-run expectations to move back to the steady state
when exogenous shocks occur. On the other hand, our estimates also provide additional
information about the anchoring of inflation expectations. In particular, we find that theconditional volatility of long-run inflation expectations and the correlation between shortrun and long-run expectations become smaller after the introduction of QQE. Overall, ourresult suggests that inflation expectations in Japan have “re-anchored” to the level around
1 percent since the introduction of QQE.
Our finding highlights three possible lessons for central banks, which may confront thechallenge to “re-anchor” inflation expectations in the future without relying on the conventional tools. First, monetary policy can be effective in raising long-run inflation expectations,even though the short-term policy rate is constrained. Second, the announcement of a higher
inflation target itself does not guarantee the re-anchoring of inflation expectations. Third,
to vary over the three groups of the periods. In the both modifications, we verify that the coefficient βτbecomes smaller after the introduction of QQE.

29

central banks should pay careful attention to the dynamic properties of long-run inflation
expectations and take quick policy actions in response to a sign of de-anchoring in the caseof highly persistent inflation expectations.

References
Aruoba, S. Boragan (2020) “Term Structures of Inflation Expectations and Real Interest
Rates,” Journal of Business and Economic Statistics, Vol. 38, No. 3, pp. 542–553.
Autrup, Søren Lejsgaard and Magdalena Grothe (2014) “Economic Surprises and Inflation
Expectations: Has Anchoring of Expectations Survived the Crisis?,” ECB Working Paper,
No. 1671.
Bai, Jushan and Pierre Perron (1998) “Estimating and Testing Linear Models with Multiple
Structural Changes,” Econometrica, Vol. 66, pp. 47–78.
(2003) “Computation and Analysis of Multiple Structural Change Models,” Journalof Applied Econometrics, Vol. 18, No. 1, pp. 1–22.
Bank of Japan (2016a) “Comprehensive Assessment: Developments in Economic Activity
and Prices as well as Policy Effects since the Introduction of Quantitative and Qualitative Monetary Easing (QQE): The Background,” URL: https://www.boj.or.jp/en/
announcements/release_2016/rel160930d.pdf.
(2016b) “New Framework for Strengthening Monetary Easing: “Quantitative andQualitative Monetary Easing with Yield Curve Control”,” URL: https://www.boj.or.
jp/en/announcements/release_2016/k160921a.pdf.
Beechey, Meredith J., Benjamin K. Johannsen, and Andrew T. Levin (2011) “Are Long-30

run Inflation Expectations Anchored More Firmly in the Euro Area than in the UnitedStates?,” American Economic Journal: Macroeconomics, Vol. 3, No. 2, pp. 104–129.
Bems, Rudolfs, Francesca Caselli, Francesco Grigoli, Bertrand Gruss, and Weicheng Lian(2018) “Expectations’ Anchoring and Inflation Persistence,” IMF Working Papers, pp.
1–30.
Buono, Ines and Sara Formai (2018) “New Evidence on the Evolution of the Anchoring of
Inflation Expectations,” Journal of Macroeconomics, Vol. 57, pp. 39–54.
Carvalho, Carlos, Stefano Eusepi, Emanuel Moench, and Bruce Preston (2017) “Anchored
Inflation Expectations,” Working Paper.
Corsello, Francesco, Stefano Neri, and Alex Tagliabracci (2019) “Anchored or De-Anchored?
That Is the Question,” Bank of Italy, Economic Research and International Relations
Area, Vol. 516, pp. 1–42.
Diebold, Francis X., Glenn D. Rudebusch, and S. Boragan Aruoba (2006) “The Macroeconomy and the Yield Curve: A Dynamic Latent Factor Approach,” Journal of Econometrics,
Vol. 131, No. 1-2, pp. 309–338.
Ehrmann, Michael (2015) “Targeting Inflation from Below: How Do Inflation Expectations
Behave?,” International Journal of Central Banking, Vol. 11, No. 4, pp. 213–249.
Galati, Gabriela, Steven Poelhekke, and Chen Zhou (2011) “Did the Crisis Affect Inflation
Expectations?,” International Journal of Central Banking, Vol. 7, No. 1, pp. 167–207.
Grishchenko, Olesya, Sarah Mouabbi, and Jean Paul Renne (2019) “Measuring Inflation
Anchoring and Uncertainty: A U.S. and Euro Area Comparison,” Journal of Money,
Credit and Banking, Vol. 51, No. 5, pp. 1053–1096.

31

Gürkaynak, Refet S, Eric Swanson, and Andrew Levin (2010) “Does Inflation Targeting
Anchor Long-run Inflation Expectations? Evidence from the U.S., UK, and Sweden,”
Journal of the European Economic Association, Vol. 8, No. 6, pp. 1208–1242.
Hattori, Masazumi and James Yetman (2017) “The Evolution of Inflation Expectations inJapan,” Journal of the Japanese and International Economies, Vol. 46, pp. 53–68.
Hogen, Yoshihiko and Ryoichi Okuma (2018) “The Anchoring of Inflation Expectations inJapan: A Learning-Approach Perspective,” Bank of Japan Working Paper Series, No.
18-E-8.
Kamada, Koichiro, Jouchi Nakajima, and Shusaku Nishiguchi (2015) “Are Household Inflation Expectations Anchored in Japan?,” Bank of Japan Working Paper Series, No.
15-E-8.
Kilian, Lutz and Christiane J S Baumeister (2016) “Lower Oil Prices and the U.S. Economy:
Is this Time Different?,” Brookings Papers on Economic Activity, pp. 287–336.
Kozicki, Sharon and Peter A Tinsley (2012) “Effective Use of Survey Information in Estimating the Evolution of Expected Inflation,” Journal of Money, Credit and Banking, Vol.
44, No. 1, pp. 145–169.
Kumar, Saten, Hassan Afrouzi, Olivier Coibion, and Yuriy Gorodnichenko (2015) “Inflation
Targeting Does Not Anchor Inflation Expectations: Evidence from Firms in New Zealand,”National Bureau of Economic Research, Vol. 46, No. 2, pp. 151–208.
Kuroda, Haruhiko (2015) “Quantitative and Qualitative Monetary Easing : Theory andPractice,” Speech at the Foreign Correspondents’ Club of Japan.
Lewis, Michelle and C. John McDermott (2016) “New Zealand’s Experience with Changing

32

its Inflation Target and the Impact on Inflation Expectations,” New Zealand Economic
Papers, Vol. 50, No. 3, pp. 343–361.
Maruyama, Toshitaka and Kenji Suganuma (2020) “Inflation Expectations Curve in Japan,”Japanese Journal of Monetary and Financial Economics, Vol. 8, pp. 1–28.
Mehrotra, Aaron and James Yetman (2018) “Decaying Expectations: What Inflation Forecasts Tell Us about the Anchoring of Inflation Expectations,” International Journal ofCentral Banking, Vol. 14, No. 5, pp. 55–101.
Nakazono, Yoshiyuki (2016) “Inflation Expectations and Monetary Policy under Disagreements,” Bank of Japan Working Paper Series, No. 16-E-1.
Nautz, Dieter and Till Strohsal (2015) “Are US Inflation Expectations Re-anchored?,” Economics Letters, Vol. 127, pp. 6–9.
Nelson, Charles R. and Andrew F. Siegel (1987) “Parsimonious Modeling of Yield Curves,”
The Journal of Business, Vol. 60, No. 4, pp. 473–489.
Nishiguchi, Shusaku, Jouchi Nakajima, and Kei Imakubo (2014) “Disagreement in Households’ Inflation Expectations and its Evolution,” Bank of Japan Review, No. 2014-E-1.
Strohsal, Till, Rafi Melnick, and Dieter Nautz (2016) “The Time-Varying Degree of Inflation
Expectations Anchoring,” Journal of Macroeconomics, Vol. 48, pp. 62–71.
Winkelried, Diego (2017) “Inferring Inflation Expectations from Fixed-Event Forecasts,”
International Journal of Central Banking, Vol. 13, No. 2, pp. 1–31.
Yetman, James (2020) “The Pass-Through from Short-horizon to Long-horizon Inflation
Expectations,” BIS Paper, No. 111e, pp. 55–66.

33

Figure 1: Individual Inflation Forecasts
2010

2013

2016

−2 −1 01

2

3

−2 −1 0

1

2

3

2007

35

30

25

20

15

10

5

0

35

30

25

20

15

10

5

0

forecast horizon
forecast

realized

medianNotes: The figure plots the distribution of reported individual inflation forecasts (percentage) in the ESP Forecast for four selected target fiscal years, 2007, 2010, 2013, and 2016.
In each graph, the horizontal axis represents the forecast horizons (h = 1 to 35). The solidline denotes the median of reported forecasts at each month and the dotted line denotes therealized value of annual inflation at each year.

34

Figure 2: Extracted Factors(a) Smoothed Level Factor(b) Smoothed Slope Factor

2

2

1.5

1.5

1

1

0.5

0.5

0
0
-0.5
-0.5
-1
-1
-1.5
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

-1.5
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017(c) Smoothed Curvature Factor
4

3

2

1

0

-1

-2
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Notes : The solid lines denote the smoothed factors and the dotted lines denote theirpointwise 95 percent confidence bands. The horizontal dashed lines in Panel (a) denote themean of the level factor in corresponding periods. The two vertical lines denote November
2008 and April 2013, respectively.

35

Figure 3: Inflation Expectations Curve before and after QQE(a) before QQE(b) after QQE

2

2

1.5

1.5

1

1

0.5

0.5

0

0

-0.5
0

24

48

72

96

-0.5

120

0

horizon

24

48

72

96

120

horizonNotes : Each solid line denotes the inflation expectation held by each individual forecaster.

36

Figure 4: Inflation Expectations Curve before and after Raising the Inflation Target(a) before raising the target(b) after raising the target

2

2

1.5

1.5

1

1

0.5

0.5

0

0

-0.5

-0.5
0

24

48

72

96

120

0

horizon

24

48

72

96

120

horizonNotes : Each solid line denotes the inflation expectation held by each individual forecaster.

37

Figure 5: Long-run Inflation Expectations

2.5
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5

Realized Monthly Inflation Rate2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Notes : Each solid line denotes the inflation expectation held by each individual forecaster. The
dotted line denotes the realized monthly year on year inflation rate (all items, less fresh foods).
The horizontal dashed lines denote the cross-sectional mean of inflation expectations in corresponding periods. The two vertical lines denotes November 2008 and April 2013, respectively.

38

Figure 6: Extracted Shocks to the Level Factor

1

0.5

0

-0.5

-1

-1.5

-2
2013

2014

2015

2016

2017

Notes : The solid line denotes the extracted exogenous shocks to the level factor. The verticalline denotes April 2013. The gray-shaded area shows the period of oil price declines.

39

Figure 7: Inflation Expectations for Selected Horizons

2.5
2
1.5
1
0.5
0
-0.5
-1
-1.5

1-Month
1-Year
2-Year
5-Year-2-2.5

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Notes : The two vertical lines denote November 2008 and April 2013, respectively.

40

Figure 8: Long-run Inflation Expectations in the Extended Model

0.1

2
1.5

0.08

1
0.5

0.06
0
-0.5
0.04
-1
-1.5

0.02

-2
-2.5

0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Notes : Each solid line denotes the inflation expectation held by each individual forecaster(left axis). The horizontal dashed lines denote the conditional variance of the level factor incorresponding periods (right axis). The two vertical lines denote November 2008 and April
2013, respectively.

41

Figure 9: Correlation between Short-run and Long-run Inflation Expectations

1
before QQE
after QQE

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

100

200

300

400

500

600

Notes : The dotted and solid lines denote the correlation between π̄t (τ ) and πt+1|t in the period
before and after the introduction of QQE, respectively.

42