Strategic Central Bank Communications:
Discourse and Game-Theoretic Analyses of the Bank of Japan’s
Monthly Reports
Kohei Kawamura∗, Yohei Kobashi†, Masato Shizume‡, and Kozo Ueda§
Imcomplete and preliminary
Abstract
We conduct a discourse analysis of the Bank of Japan’s monthly reports and examine
their characteristics in relation to business cycles. We find that the difference between the
numbers of positive and negative expressions in the reports leads the leading index of the
economy by approximately three months, which suggests that the central bank’s reports
have some superior information about the state of the economy. Moreover, ambigious words
tend to appear more frequently with negative expressions. Using a simple persuasion game
model, we argue that the use of ambiguity in communication by the central bank can be
seen as strategic information revelation when the central bank has an incentive to bias the
reports (and hence beliefs in the market) upwards.
Keywords: monetary policy; transparency; natural language processing; modality;
latent Dirichlet allocation (LDA); verifiable disclosure model
JEL classification: D78, D82, E58, E61
∗The University of Edinburgh†Waseda University‡Waseda University§Waseda University (E-mail: [email protected]). The authors appreciate comments from seminar partic-
ipants at the Bank of Japan and Waseda University. All errors are our own.
1
1 Introduction
Central banks not only implement monetary policy but also provide a significant amount of
information for the market (Blinder [2004], Eijffinger and Geraats [2006]). Indeed, most of
publications by central banks are not solely about monetary policy but about data and analyses
on the current economic state. It has been widely recognized that central banks use various
communication channels to influence the expectations in the market so as to enhance the
effectiveness of their monetary policy. Meanwhile, it is not readily obvious whether central
banks reveal all information they have as it is. In particular, central banks can communicate
strategically and be selective about the types of information they disclose, if not telling lies
due to accountability and fiduciary requirements. This concern would be especially important
when central banks’ objectives (e.g. keeping inflation/deflation under control, stimulating
the economy) may not be aligned completely with those of market participants and possibly
governments.
In this paper, we study a central bank’s communication strategy by looking at how ex-
pressions used in published reports are related to the state of the economy. Specifically, we
conduct discourse analyses using the Bank of Japan’s Monthly Report of Recent Economic and
Financial Developments (the monthly reports, hereafter) from January 1998 to March 2015.
Employing a natural language processing method, we first classify expressions in the monthly
reports according to polarity (whether an expression is positive, negative, or neutral) and
modality (whether an expression is clear-cut, ambiguous, or subjective), among others. We
find that the difference between the numbers of positive and negative expressions in the re-
ports leads the leading index of the economy by approximately three months, which suggests
that the central bank and the reports have some superior information about the state of the
economy. Moreover, ambiguous expressions are more likely to be used when the economy is in a
recession. For example, when the leading index of the economy is low, monthly reports contain
a larger number of expressions with negative tones (e.g. “fall”) and the modality expressions
that indicate likelihood (e.g., “seem” and “should”) rather than certitude. We further confirm
using a latent Dirichlet allocation (LDA) model that ambiguous expressions are indeed more
likely to be used in sentences that contain negative expressions. This suggests the possibil-
ity that the central bank deliberately introduces ambiguity into sentences conveying negative
information about the economy.
Second, we develop a simple persuasion game model to understand the empirical obser-
vations as a consequence of strategic communication. In the model the central bank always
obtains coarse information about the state of the economy but may or may not find precise
information. Moreover, the central bank has an upward bias: it wants the market to hold
2
optimistic beliefs when the ongoing inflation rate is lower than the target, which has been
actually the case in the data period of our empirical analysis. We demonstrate that when the
state of the economy is bad, the central bank only discloses coarse information. In other words,
negative reports become ambiguous in order to avoid the market to be very pessimistic. On
the other hand, if the central bank receives the precise signal that the economy is good, the
signal is disclosed as it makes the market belief more optimistic than when it is withheld. We
argue that the central bank’s equilibrium reporting strategy is consistent with what we observe
in the monthly reports.
Our study contributes to existing literature in illustrating and explaining the strategic
aspect of central banks’ communication. As the first strand of relevant papers, there are a
rapidly increasing number of studies on central banks’ communications using discourse analyses.
For example, Boukus and Rosenberg (2006), Hendry and Madeley (2010), Hendry (2012), Apel
and Grimaldi (2012) analyze central banks’ communications in the Federal Reserve, the Bank
of Canada, and Riksbank, and their relevance to the real economy and/or effects on financial
markets. Born, Ehrmann, and Fratzscher (2014) find that financial stability reports released by
central banks have positive effects on financial markets when their views are optimistic but no
effect when they are pessimistic. Our study complements these previous studies by emphasizing
the usage of ambiguity and explaining its rationale using a game theory. In particular, the above
finding by Born, Ehrmann, and Fratzscher (2014) as well as our empirical results turns out
to be consistent with the predictions from our game theoretic model. It is also worth noting
that, to the best of our knowledge, there is no academic study regarding the Bank of Japan’s
communication. Furthermore, our study may be valuable to financial market participants who
are eager to know the intents of central banks and forecast the future course of monetary policy.
The LDA is employed in economics, particularly for central banks’ communications, previously
by Hansen, McMahon, and Prat (2014).1
In view of natural language processing, our analysis contributes to the field of semantic
analyses in pointing out the importance of modality in drawing the sender’s assessments and
intentions in the actual policy front. A hegemony view is often taken as granted, that is,
communications by the authority are regarded as perfectly credible (Gramsci [1971]), although
there exists critical discourse analyses by Fairclough (1989) and van Dijk (2008). Our study
challenges the hegemony view by considering a possibility that a receiver does not necessarily
believe what a sender says.
This paper is structured as follows. Section 2 explains our data and results from discourse
analyses. Section 3 proposes a game theoretic model to explain the Bank of Japan’s communi-
1They investigate how discussions and decision making proceed using the Federal Reserve’s transcripts.
3
cation strategy. Section 4 concludes.
2 Data and Results from Discourse Analyses
2.1 Data
Our data are the Bank of Japan’s monthly reports in a Japanese version from January 1998 to
March 2015 (207 in total). The Bank of Japan started to publish monthly reports in January
1998 when it strengthened independence by a law amendment. Each report is released on the
next day of a monetary policy meeting. The Bank’s view that is a summary of the report and
authorized by committee members is released on the same day of the monetary policy meeting.
The full body of the report is written by the staff of the Bank of Japan, not by committee
members.
For our study, the Bank of Japan’s monthly reports have mainly three advantages over
other types of communication methods. First, their release is monthly, which is highly frequent.
Most central banks in advanced economies hold monetary policy meetings around eight times
a year. Consequently, their policy statements and reports are released less frequently than
monthly. True, other types of communication methods entail richer information. For example,
minutes and transcripts of monetary policy meetings convey what policy committee members
discussed and Q&A’s by Governor may convey non-verbal information. However, to analyze
relationships between communications and business cycles in a timely manner, in particular,
their lead-lag relationships, it is ideal to have data of a high frequency. Second, the Bank of
Japan’s monthly reports are rich enough for us to evaluate its assessments on the economy.
Every monthly report has typically around 150 sentences. Some central banks such as the
Federal Reserve and the Bank of England release statements immediately after meetings but
their length is much shorter.2 Moreover, because the Bank of Japan have been published since
1998, data are ample enough to obtain statistically significant results. Third, the format of
monthly reports is clear and stylized. Their sections are always written in the order of summary,
economic developments, prices, and financial developments. The summary is further divided
into two: current situations and forecasts. Moreover, each section makes routine explanations.
For example, the summary begins with a sentence that overviews current economic situations,
followed by detailed assessments on the overseas economies, exports, fixed business investment,
and so forth in the almost same order. Such a clear format enhances the accuracy of our natural
language processing.3
2The Bank of England enriched their communications in August 2015.3To our great disappointment, however, after we initiated this research, the Bank of Japan in June 2015
decided to discontinue the publication of monthly reports from January 2016. The number of monetary policy
4
We use monthly reports in Japanese although an English version exists. This is because
the Bank of Japan’s decision-making and communication with the public are made in Japanese
and because an English version is based on a Japanese version and translated to English with
some delay (a few days).4 We provide translation tables in Appendix.
2.2 Basic Statistics
Table 1 shows basic statistics of the Bank of Japan’s monthly reports. Monthly reports have
the average sentences of 32, 79, 20, and 24, respectively, for the sections of summary, economic
developments, prices, and financial developments. As for a morpheme that represents the
smallest grammatical unit, the number of morphemes per sentence is roughly the same among
the four sections, which is around 30.
From now on, we focus only on the section of summary. Although it reduces the sample
size, it turns out enough to obtain statistically significant results. Time series developments in
the number of sentences and morphemes are displayed in Figure 1. It shows a clear level shift
in October 2003, although the number of morphemes per sentence stays almost constant. This
is when the Bank of Japan decided to enhance monetary policy transparency. To mitigate the
effect of this policy shift, in analyses below, we normalize the number of expressions by the
number of morphemes except that we do the number of morphemes by the number of sentences.
Table 1: Basic Statistics
Sentences Morphemes Mor/senmean (s.d.) mean (s.d.) mean (s.d.)
Summary 31.69 (8.24) 940.14 (338.82) 29.25 (4.47)- present 22.61 (5.92) 629.11 (224.66) 27.44 (4.07)- forecast 9.08 (2.84) 311.03 (129.84) 33.89 (6.79)Economics 79.12 (27.88) 2848.83 (1110.44) 36.12 (4.19)Prices 19.55 (8.07) 704.60 (300.63) 36.80 (6.24)Financial 24.35 (3.58) 753.33 (211.22) 30.52 (4.46)
meetings is reduced from 14 times a year to 8 and the publication of the Outlook for Economic Activity andPrices increases from semi-annually to quarterly. The discontinuation of monthly reports may decrease theinterests of financial market participants who are eager to know the intent of the Bank of Japan from ourstudy. Nevertheless, we believe that our study has still a very important contribution to them in illustrating thecharacteristics of the Bank of Japan’s communication and intrinsic motives behind, which is likely to be passedon to other types of communication methods.
4Obviously, Japanese is different from English. One particularly important property is that Japanese isregarded as rich language with regard to expressing subjectivity in Japanese linguistics as we explain in Section2.3.2. The reason and influence of this difference are to be explored in the future.
5
0
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1998
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Sentences (left axis)Morphemes/sentences (left axis)Morphemes (right axis)
Figure 1: Time Series Developments in the Number of Sentences and Morphemes
2.3 Classifications
Next we classify the morphemes of the monthly reports by various criteria, namely, polarity,
modality, a part of speech, and other miscellaneous criteria.
2.3.1 Polarity
The first is the polarity that indicates positive, negative, or even tones in morphemes. To this
end, we adopt the widely-accepted polarity criterion developed by Inui and Okazaki (XXX).5
Both positive and negative tones are further divided into two types: objective experiences and
subjective evaluations.
For each category, Table 2 illustrates the five most frequent morphemes where a number in
the right of morphemes indicates their counts. As the table shows, classifications are not perfect.
True, it seems acceptable that “increase” is categorized to even because it is positive when it
refers to an increase in demand but negative when it does to an increase in debts. However,
maybe different from the common sense of economists, “demand,” “fund,” and “economy“
are all categorized to positive. Japanese “tame” is used for two usages: “is good for” or “is
because of (reason).” Of the two, only the former contains a positive tone. Despite these
5See http://www.cl.ecei.tohoku.ac.jp/index.php?Open%20Resources%2FJapanese%20Sentiment%20Polarity%20Dictionary(in Japanese).
6
caveats, the reason we use this criterion is to maintain objectivity. Moreover, results drawn
from this criterion turns out to be indicative about business cycles.
Table 3 shows the basic statistics of polarity expressions. Evaluation morphemes, both
positive and negative, are less frequently used in monthly reports than experience ones. Com-
paring expressions in current situations and those in forecasts, we find that the latter tends to
have more positive (less negative) morphemes than the former. Indeed, the differene between
positive and negative morphemes, which is shown in the last column, is significantly larger for
forecasts than that for current situations at a one percent level.
Table 2: Top Five Polarity Expressions
Positive Negative Evenexperience # evaluation # experience # evaluation # #
demand 1062 good 205 fall 522 excess 147 invest 1166improve 733 good/reason 92 decline 397 weak 131 increase 1121
fund 626 ease 85 price 335 minus 108 environment 891recovery 501 ample 62 worsen 181 sluggish 88 modest 802economy 478 grow 60 cost 150 weak 45 produce 541
Table 3: Basic Statistics of Polarity Expressions
Positive Negative Even Positive-negativeexperience evaluation experience evaluation
Summary 0.0390 0.0040 0.0154 0.0030 0.0964 0.0246(0.0065) (0.0019) (0.0079) (0.0018) (0.0109) (0.0101)
- present 0.0347 0.0043 0.0148 0.0022 0.0954 0.0219(0.0076) (0.0019) (0.0084) (0.0014) (0.0116) (0.0120)
- forecast 0.0469 0.0036 0.0156 0.0045 0.0985 0.0307(0.0110) (0.0040) (0.0094) (0.0041) (0.0139) (0.0138)
Note: Ratio to total morphemes. Figures in parentheses represent standard deviations.
2.3.2 Modality
The second classification is modality. Specifically, we focus on the modality of truth judgement
and categorize it into three types: high probability, low probability, and unreal, opposed to
certitude. Table 4 shows expressions in each category and their basic statistics. High probability
expressions such as “seem” and “appear” suggest that a possibility that an event in the sentence
occurs is high, but not 100%. An example of low probability expressions is “may.” Unreal
expressions include “should” and “it is important to.” When they are used, a possibility that
an event occurs is nearly zero.
7
Modality is considered to be highly indicative of ambiguity. Modality is drawn from a
verb, which is located almost always at the very end of each sentence in Japanese. It has been
suggested in Japanese linguistics that Japanese syntactic structure is strongly correlated with
semantic hierarchical structure defined by the modality theory (Narrog [2009]). This structural
correlation implies that modality plays a more important role in conveying a writer’s perspective
in Japanese than that in English does.6
It is worth noting that we select the criterion of modality according to our human coding.
We choose all the end-of-sentence expressions that appear at least ten times in monthly re-
ports. Then, referring to previous studies (XXX), we classify them into high probability, low
probability, unreal, and certitude.
The table suggests that modality is more frequently used in forecasts than in current situa-
tions. This is natural because forecasts are uncertain, requiring the use of “seem” or “appear.”
Low probability expressions are never used in current situations.
Table 4: Modality Expressions
High probability Low probability UnrealExamples (seems, appear, expected, (may, warrant (should, it is
considered, forecasted, likely) careful monitoring) important to)
Summary 0.0090 0.0001 0.0007(0.0028) (0.0003) (0.0010)
- present 0.0006 0.0000 0.0004(0.0010) (0.0000) (0.0007)
- forecast 0.0261 0.0003 0.0014(0.0090) (0.0009) (0.0020)
Note: Ratio to total morphemes. Figures in parentheses represent standard deviations.
2.3.3 A Part of Speech
Third, we classify morphemes by a part of speech. A part of speech is a rough but reasonable
indication of contents. In Japanese, it consists of nouns, verb-type nouns, verbs, adjectives,
adjectival nouns, adverbs, particles, conjunctions, and suffixes. PLEASE EXPLAIN THEM.
Lexical units are the sum of nouns, verb-type nouns, verbs, adjectives, adjectival nouns, and
adverbs, while functional (grammatical) units are the sum of other parts of speech. A lexical
density is defined by a fraction of lexical units in total, which indicates the density of contents
according to Halliday (1985). Written languages generally have a higher lexical density than
spoken languages. Table 5 shows the basic statistics of a part of speech. In the order of
6One caution is that ambiguous expressions do not necessarily come from a writer’s subjective perspective.Expressions may become ambiguous because uncertainty surrounding the writer is large. In this empirical partof the study, we do not specify a fundamental reason for ambiguity.
8
appearance, particles, nouns, conjunctions, and verb-type nouns are frequently used, while
adjectives are the least.
Table 5: A Part of Speech
Nouns Verb-type nouns Verbs Adjectives Adjectival nouns Adverbs
Summary 0.2038 0.1067 0.0972 0.0081 0.0164 0.0374(0.0107) (0.0066) (0.0067) (0.0040) (0.0051) (0.0062)
- present 0.2143 0.1017 0.0935 0.0067 0.0130 0.0373(0.0129) (0.0077) (0.0065) (0.0045) (0.0055) (0.0067)
- forecast 0.1829 0.1170 0.1047 0.0106 0.0234 0.0377(0.0193) (0.0141) (0.0109) (0.0054) (0.0066) (0.0102)
Particles Conjunctions SuffixesSummary 0.5303 0.1094 0.0709
(0.0089) (0.0075) (0.0079)- present 0.5335 0.1113 0.0722
(0.0099) (0.0084) (0.0083)- forecast 0.5238 0.1056 0.0679
(0.0182) (0.0138) (0.0117)
Note: Ratio to total morphemes. Figures in parentheses represent standard deviations.
2.3.4 Other Miscellaneous Criteria
We also collect the expressions other than the above that are considered to be associated
with either polarity or ambiguity. They are (1) “increase,” (2) “decrease,” (3) conjunctions
with negative tones (e.g., “although,” “but”), (4) adverbs with clear tones (e.g., “clear”), (5)
adverbs with ambiguous tones (e.g., “slight,” “a bit,” “in general,” “almost”), (6) nouns with
ambiguous tones (e.g., “trend”), (7) “etc,” and (8) Japanese “wa.” We pick up (1) and (2)
specifically because they are particularly indicative of polarity. Expressions (5) to (7) entail
ambiguous tones. As for (8), Japanese “wa” is one of the most routinely used particle that is
attached to a subject. For example, “demand decreases” is translated to “juyou (demand) wa
genshou-shiteiru (decreases).”
2.4 Correlations with Business Cycles
Using expressions classified in the aforementioned method, we compare them with macroeco-
nomic data.
2.4.1 Macroeconomic Data
We use macroeconomic data that indicate Japan’s business cycles and/or relate to monetary
policy. Their frequency is all monthly. First, we use three kinds of composite indexes complied
9
by the Cabinet Office: the leading, coincident, and lagging indexes. The leading index, on which
we focus most, is compiled by combining 11 variables such as new orders for machinery, housing
construction started, the commodity price index, and the stock price. It leads the composite
index by a quarter. The composite indexes are published about 40 days after the month
concerned ends (e.g;, around March 10 for the index of January). Thus, to align timing with
monthly reports, we use two-month lagged composite indexes as the real-time data. The second
data is the year-on-year inflation rate based on the consumer price index (CPI). Again, we make
it real-timed by using two-month lagged series considering 30 days delay in its publication.
Moreover, we take account of revisions in the CPI which occurs every five years. We also
eliminate effects of consumption tax increases in 1997 and 2014. Third, we construct a monetary
policy change dummy from actual monetary policy changes. The variable takes one when policy
is tightened and minus one when policy is eased. Otherwise, it is zero. Some policy changes
may have been anticipated before monetary policy meetings, so it does not necessarily reflect
a monetary policy shock. Because there have been a number of small monetary easing in our
sample period, we construct the alternative dummy variable, which we call the big change
dummy, by choosing rather big policy changes. The dummy data are explained in Appendix.
2.4.2 Correlations
To compare expressions in monthly reports with macroeconomic data, we take a simple ap-
proach to look at their correlations. This is a basic and clear way to our aim. Although
correlations are silent about causality, a causality is likely to go from business cycles to the
Bank of Japan’s communications at least in the monthly time horizon. In other words, macroe-
conomic data on business cycles are considered to be exogenous, although monetary policy is
surely to influence the macroeconomy with a lag of several months to a couple of years. Spuri-
ous correlations tend to arise when data are non-stationary, but our data are stationary. That
said, we admit that our method is not immune to several caveats, so we check robustness in
the next subsection. A correlation is significantly different from zero at a one and five percent
level, if its absolute value exceeds 0.179 and 0.137, respectively for the sample size of 207.
Table 6 shows correlations between the expressions related to current situations and the
macroeconomic data. Several findings are worth noting. First, polarity expressions with posi-
tive (negative) tones are positively (negatively) correlated with the leading index, that is, the
future economy. This supports the usefulness of the polarity criterion developed by Inui and
Okazaki (XXX), although it is not designed to be applied to economics. In particular, dif-
ferences of positive and negative expressions (denoted by “pos neg” in the table) are highly
informative. Moreover we find that the words of “increase” and “decrease” are correlated with
10
the future economy positively and negatively, respectively. Conjunctions with negative tones
are also negatively correlated.
Second, expressions associated with ambiguity are negatively correlated with the future
economy. This is illustrated by a number of results. First, the ratio of morphemes to sentences
is negatively correlated with the leading index. In short, the future economy is worse, as
words per sentence are longer. Long explanations may suggest, in part, detailed information
revelation about the economy, but at the same time, increase unnecessary excuses and impair
clarity. Second, modality is negatively correlated with the leading index. When monthly reports
use expressions with high probability or unreal tones in the section of the current situation,
the future economy tends to deteriorate. Third, while nouns and verbs are indicative of an
increase in the leading index, adjectives are indicative of its decrease. These parts of speech
are all lexical, but nouns and verbs are considered to be professional and objective and they
are pro-cyclical. By contrast, adjectives are subjective and ambiguous and they are counter-
cyclical. Fourth, the use of “etc” is negatively correlated with the leading index. Clearly, this
word increases ambiguity.
Third, a result specific to Japanese is that the use of “wa” is highly correlated with the
leading index. As was previously explained, “wa” is one of the most routinely used particle that
is attached to a subject. This result implies that the economy is strong when the fraction of
routine expressions is high. By contrast, under unprecedented circumstances, the central bank
needs to make longer and unorthodox explanations, which reduce the usage of “wa.” Moreover,
a peculiar property of Japanese is that a sentence does not always require a subject. Japanese
allows to omit a subject and leaves the judgment as to who is the subject to readers or listeners.
This functions to increase ambiguity. Therefore, a less frequent use of “wa” implies greater
ambiguity.
Fourth, of the three composite indexes, the leading index tends to be most highly correlated
with the expressions such as polarity and modality. The coincident index is the second and the
lagging index is the least. In this respect, the Bank of Japan in monthly reports discusses the
future economy of about a quarter ahead, despite in the section of the current situation. This
is a natural thing for monetary policy because it needs to be forward looking.
Fifth, correlations with the inflation rate are lower in their absolute size than those with
the leading index. Polarity expressions have of little value in connecting with inflation develop-
ments. However, the words of “increase” and “decrease” are significantly correlated with the
inflation rate positively and negatively, respectively.
Finally, correlations with the monetary policy change dummy are examined. Differences
of positive and negative expressions are positively correlated with the dummy. That is, when
11
positive expressions are more used than negative expressions, the Bank of Japan tends to
tighten monetary policy.
Table 6: Correlations (Current Situations)
leading coincident lagging inflation mdummy mbigdummy
mor/sen -0.73 -0.61 -0.46 -0.14 -0.05 -0.06high prob -0.37 -0.33 -0.15 0.14 0.04 -0.03low probunreal -0.61 -0.44 -0.31 -0.08 -0.09 -0.09
pos neg 0.57 0.38 -0.03 -0.14 0.20 0.15pos exp -0.11 -0.27 -0.54 -0.28 0.03 -0.05pos eval 0.45 0.48 0.35 0.18 0.18 0.20neg exp -0.82 -0.67 -0.35 0.02 -0.18 -0.17neg eval -0.18 -0.02 -0.06 0.01 -0.05 -0.12increase 0.65 0.67 0.69 0.21 0.28 0.24decrease -0.41 -0.40 -0.10 -0.29 -0.06 -0.01
noun 0.41 0.34 0.31 0.09 -0.05 0.04verb noun -0.37 -0.23 0.04 -0.09 0.04 0.04
verb 0.28 0.14 -0.05 0.02 0.05 0.03adj -0.72 -0.79 -0.62 -0.41 -0.18 -0.13
adj noun 0.12 0.34 0.46 0.39 0.14 0.11adv -0.16 -0.16 -0.22 -0.11 -0.04 0.00
func word -0.06 -0.07 -0.23 -0.02 0.03 -0.10par 0.18 0.03 -0.02 -0.27 -0.01 0.00conj -0.06 -0.10 0.12 -0.06 0.09 -0.04suf 0.10 0.17 0.08 0.20 0.02 0.04
conj neg -0.44 -0.55 -0.46 -0.45 -0.04 -0.12adv clear -0.15 -0.34 -0.36 -0.46 -0.10 0.01adv amb 0.18 0.16 -0.07 -0.16 -0.11 -0.02
etc -0.55 -0.39 -0.24 -0.10 -0.08 -0.04noun amb 0.38 0.39 0.21 0.09 0.06 0.06
wa 0.67 0.60 0.56 0.04 0.18 0.13
Next, we turn to the statements on forecasts. Table 6 shows correlations between the
expressions related to forecasts and the macroeconomic data. This suggests that almost all the
previous results hold true except for some noteworthy differences. The first difference is a role of
modality. For the section of forecasts, modality expressions with high probability are positively
correlated with the leading index. This is natural because high probability expressions such
as “seem” and “likely” are tightly linked to forecasts. On the other hand, expressions with
low probability and unreal are negatively correlated with the leading index. In other words,
ambiguous expressions using “may” or “should” are counter-cyclical. Second, polarity remains
to be a useful indicator for the future economy, but the size of correlations is lower in forecasts
than in current situations. This is surprising to us because the section on forecasts should be
more concerned about the future economy than that on current situations. Readers may think
that this is because the Bank of Japan talks about farther future economy in the sections of
forecasts, but this is not the case as we will see soon below.
12
Table 7: Correlations (Forecasts)
leading coincident lagging inflation mdummy mbigdummy
mor/sen -0.54 -0.40 -0.35 -0.06 -0.09 -0.06high prob 0.60 0.42 0.33 0.10 0.09 0.08low prob -0.39 -0.28 -0.19 -0.22 -0.05 -0.03unreal -0.31 -0.17 -0.09 0.08 0.01 -0.01
pos neg 0.14 0.16 0.05 0.19 0.21 0.06pos exp -0.21 -0.13 0.06 0.01 0.19 0.07pos eval -0.33 -0.25 -0.26 0.15 -0.07 -0.13neg exp -0.60 -0.45 -0.09 0.01 -0.06 -0.04neg eval 0.02 -0.05 -0.01 -0.28 0.01 -0.01increase 0.79 0.67 0.54 0.12 0.24 0.20decrease -0.01 0.11 0.31 0.04 -0.08 0.01
noun 0.07 0.25 0.51 0.43 0.09 0.06verb noun 0.25 0.22 0.03 0.10 0.08 0.12
verb 0.11 -0.03 -0.27 -0.08 -0.18 -0.10adj -0.21 -0.22 -0.06 -0.13 -0.04 -0.05
adj noun 0.22 0.29 0.25 0.19 0.17 0.06adv -0.30 -0.30 -0.36 -0.26 -0.23 -0.18
func word -0.19 -0.28 -0.28 -0.36 0.03 0.00par -0.15 -0.11 0.04 0.17 0.04 -0.01conj 0.42 0.23 0.08 -0.22 0.06 0.06suf 0.12 0.15 0.20 -0.13 0.03 -0.02
conj neg -0.04 -0.06 -0.18 -0.17 -0.05 -0.03adv clear -0.08 -0.12 -0.26 -0.23 -0.12 -0.03adv amb -0.19 -0.22 -0.31 -0.21 -0.20 -0.06
etc -0.01 -0.01 -0.12 0.02 0.15 0.07noun amb 0.67 0.61 0.33 0.11 0.02 0.08
wa 0.35 0.19 0.02 -0.02 -0.05 -0.04
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pos-neg (current) pos-neg (forecast) leading
Figure 2: Time Series Developments in the Polarity Expressions (Left) and the Leading Index(Right)
Figure 2 demonstrates time series developments in the polarity expressions and the leading
index. As for the polarity expressions, we plot differences of positive and negative expressions
for both current situations and forecasts. Clearly, they are highly correlated with the leading
index.
Figure 3 shows correlations with the polarity expressions and the leading index. To study
their lead-lag relations, we compute the correlations using the leading index that differs in
timing from minus six to plus six months. The horizontal axis represents the month. For
example, +1 indicates a correlation between the polarity expressions and the leading index
with a one-month lead. This figure shows that correlations are peaked at x = 3 for both
current situations and forecasts. In other words, polarity expressions in monthly reports lead
the leading index by three months. This implies that the Bank of Japan has a forecasting power
about the three-month ahead leading index. Even if we exclude the effect of the two-month
delay in the publication of the leading index, the Bank of Japan maintains a forecasting power
by one month. Another finding is that the timing of the peak is the same between current
situations and forecasts. The section of forecasts does not necessarily discuss the economy of
a farther future horizon than that of current situations. Even worse, the correlation is lower.
Figure 4 illustrates time series developments in the modality expressions and the leading
index. The modality expressions are those of high probability for both current situations and
14
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Correlations bw positive-negative and leading index(+i)
Current Forecast
Figure 3: Correlations with the Polarity Expressions and the Leading Index
forecasts. This figure suggests that the modality for current situations increases when the
leading index falls. Here we give three illustrating examples. First, during the financial crisis
of 1998, the Bank of Japan used such expressions as “may be attributed to” and “appear
to.” Aaccording to its English version, the monthly report of July 1998 stated “stock prices
and yields on long-term government bonds have rebounded since mid-June 1998. This may be
attributed to a slight recovery in market sentiment, although still weak. . . ” (Italics are added
by the quoters.) It also said “growth in M2+CDs has been slowing. . . These developments
appear to strongly reflect the further decline in credit demand of private firms. . . ” Second, the
report of May 2009 uses “seem” in the aftermath of the Lehman shock as “It seems that firms’
funding costs ... have remained more or less unchanged at low levels.” The third example is
“appear to,” which is used from May 2013 to the end of our sample: “Inflation expectations
appear to be rising on the whole.” During this period, the economy was relatively in a better
shape due to the large-scale monetary easing introduced in April 2013. The stock price jumped
up and the exchange rate depreciated. However, the inflation rate and its expectations were
well below the Bank of Japan’s inflation target, two percent, although the Bank of Japan
promised to achieve this level within two years. Such a background seems to induce the Bank
of Japan to use the expression of “appear.” Moreover, the expression of “on the whole” in the
same sentence is also ambiguous.7
7We notice that the nuance in the English version differ at times. For example, the Japanese version in May
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Figure 4: Time Series Developments in the Modality Expressions (Left) and the Leading Index(Right)
The figure also implies a caveat of our study. Although high probability expressions are
used for forecasts every month, they are not used for current situations every month. As this
example illustrates, some expressions are sparse, which may yield a bias in looking at simple
correlations.
2.4.3 Robustness
Because correlations are too simple statistics to obtain robust conclusions, we check robustness
in various ways. First, we compute correlations using a time difference. Although the variables
we use are stationary, some of them tend to obey a slow moving process and embedding a
persistence. To resolve this and to check robustness, we take monthly differences for all the
variables and compute correlations. We confirm that monthly changes in polarity expressions
remain to be significantly correlated with monthly changes in the leading index. However, many
of our results regarding ambiguity disappear. In particular, neither words length per sentence
nor modality is no longer correlated with the leading index when their monthly changes are
taken.
2009 exploits two more modality expressions “considered” and “seem” in the following sentences. However, suchexpressions disappear in the English version. This may reflect a large importance of modality in Japanese tojudge the writer’s perspective than in English.
16
Second, we conduct the Granger causality test for the same reason above. Specifically, we
choose two variables: the leading index and the polarity expression defined by the difference of
positive and negative expressions for current situations. The AIC chooses the lag of six months;
the polarity expression Granger-causes leading index with a one percent significance; and the
leading index Granger-causes the polarity expression only with a 10 percent significance.
Third we confirm that our results are robust to the split of sample at October 2003. This
is when the Bank of Japan decided to enhance monetary policy transparency and reduced the
number of sentences considerably.
Fourth, although we have computed correlations between two variables, more than two
variables are likely to interact each other. In particular, some expressions in monthly reports
may be used together depending on economic circumstances. For example, both negative
polarity expressions and modality expressions may tend to be used in monthly reports when
the economy is in a recession. To tackle this issue, we employ the LDA method in the next
subsection.
2.5 Latent Dirichlet Allocation (LDA)
2.5.1 Methods
The LDA enables us to study what kind of expressions are used together and under what
circumstances. SOME EARLIER STUDIES AND/OR BRIEF MATHEMATHICAL EXPLA-
NATIONS.8 The LDA makes use of a generative probabilistic model for text corpora. The
model consists of a finite mixture over an underlying set of topics, where topics are the latent
variables that represent properties common to a number of expressions. Put intuitively, the
topics are like the principal components of expressions. The LDA is Bayesian unsupervised
learning and does not rely on the supervised classification that is based on human’s subjective
judgments. An important advantage of the LDA over other similar methods such as factor
and cluster analysis is that the LDA allows one expression to be categorized in different topics.
This appears plausible in that the same expression such as “increase” and “seem” can be used
in different contexts depending on economic circumstances and objectives. Moreover, the LDA
can resolve high-dimensional and sparse data (XXX), which is important in our data.
More concretely, we apply the LDA to our data in the following manner. We use expressions
at a low level, that is, in the unit of morphemes, not polarity, modality, or a part of speech.
Expressions are sorted in the order of those associated with modality, polarity, and a part of
speech. No double counting is allowed. For example, once “increase” is selected as a polarity
8See also Hansen, McMahon, and Prat (2014). ANY Difference???
17
expression, it is not selected as an expression of either noun or verb. The number of topics is
chosen from the AIC.
While many variants of models can be proposed, as the benchmark, we consider a rather
parsimonious model which consists of expressions associated with modality, polarity, adverbs,
and adjectives sorted in this order. The minimum appearance of five times is required for
expressions to be selected.
2.5.2 Results
First, we report a result for the section of current situations in Table 8. The AIC chooses four
topics. In the table, each column indicates a topic. The second row indicates the label of each
topic, which we name from selected modality and polarity. The third to ninth rows show the
concrete expressions that characterize the topic in the order of their selection frequency. The
rows from the tenth represent correlations with the time-series development in each topic and
macroeconomic data.
This table illustrates that Topic 2 is pro-cyclical with business cycles, while Topics 3 and
4 are counter-cyclical. Topic 2 is positively correlated with both the leading index and the
inflation rate. At the same time, Topic 2 consists of expressions with positive tones such as
“increase” and “ease” although they are categorized as even according to the polarity criterion.
In this topic, no modality expression is selected. On the other hand, Topics 3 and 4 are
negatively correlated with both the leading index and the inflation rate. Although Topic 3
includes positive expressions, they do not show any positive tones if we look at the expressions
closely (e.g., “demand,” “fund,” and “credit”). Rather, Topics 3 and 4 consist of negative
expressions such as “fall” and “worsen.” Moreover, Topic 3 embeds modality expressions
related to high probability and unreal. Such expressions like “seem” and “should” indicate
ambiguity and/or a lack of objectivity. Four topics also include many expressions belonging to
adverbs and adjectives. However, no noteworthy property is observed.
Next, we report a result for the section of forecasts in Table 9. Similar results can be found.
Four topics are selected from the AIC, of which Topics 1 and 2 are counter-cyclical and Topics
3 and 4 are pro-cyclical. Topics 1 and 2 consists of not only negative expressions but also
modality ones. In particular, modality expressions associated with low probability and unreal
are linked to these topics. Its examples are “attention should be paid to the possibility,” “may,”
and “should.” Again these expressions have ambiguous tones. When these tones are used, the
economy is likely to be weak with respect to the leading index and the inflation rate. By
contrast, Topics 3 and 4 are positively correlated with the macroeconomic data. These topics
consist of even polarity expressions, but they actually have positive tones (e.g. “increase”). For
18
Table 8: LDA Results (Current Situations)
Topic 1 2 3 4Label positive; even even high prob.; unreal;
positive; negativenegative
high prob. seem, appearlow prob.unreal shouldpositive fund; demand;
improve; creditdemand; good; ef-fect; money; profit;economy; income
demand; fund; creditdemand; interest;credit; activity;service; income;good/reason; resolve
negative fall; weak; weak; de-cline; weak; atten-tion; cost; excess;worsen; expenditure
price; fall; decline;worsen; subjue; slug-gish; financial posi-tions; severe; reces-sion; risk
even envirnonment;ease; level; issue;invest; increase;finance; modest;thing/maturity;under/middle
increase; environ-ment; invest; level;modest; issue; state;thing/maturity;ease; finance
others (top three) adv: previous year;adv: meanwhile;adv: generally; andso on
adv: previous year;adv: meanwhile;adv: generally; andso on
adv: recently; adv:still; adv: slight; andso on
adv: previous year;adv: still; adv: se-vere; and so on
Correlations withleading 0.068 0.514 -0.553 -0.537coincident -0.091 0.462 -0.421 -0.444lagging -0.227 0.393 -0.419 -0.343inflation -0.087 0.199 -0.249 -0.195mdummy -0.042 0.035 -0.061 -0.062mbigdummy -0.039 0.011 -0.038 -0.018
19
the section of forecasts, modality expressions associated with high probability appear together
with positive ones. As was stated, this is natural because this section discusses the future and
thus often use the expressions such as “seem” and “forecasted.”
Table 9: LDA Results (Forecasts)
Topic 1 2 3 4Label low prob; positive;
negativelow prob; unreal;negative
high prob; even high prob; even
high prob. seem, appear; con-sidered; forecasted;likely
seem, appear; fore-casted; considered;likely
low prob. attention should bepaid to the possibil-ity of; may
may
unreal should; is importantto
positive economy; demand;improve; supply anddemand conditions;information; profit;technology; income;progress; capital
negative fall; price; worsen;weak; risk; weak; ad-verse effect; uncer-tain; sluggish; uncer-tain
decline; excess;price; fall; subjue;expenditure; restruc-turing; attention;pass through; minus
even modest; increase; in-vest; trend; consume;effect; produce; con-sumer; invest; em-ployment
increase; trend; in-vest; modest; pro-duce; effect; employ-ment; consumer; ex-pand; under/middle
others (top three) adv: for the time be-ing; adv: whole; adv:still; and so on
adv: future; adv: forthe time being; adv:still; and so on
adv: for the time be-ing; adv: previousyear; adv: gradual;and so on
adv: for the time be-ing; adv: meanwhile;adv: previous year;and so on
Correlations withleading -0.495 -0.484 0.263 0.473coincident -0.433 -0.394 0.192 0.459lagging -0.364 -0.405 -0.062 0.548inflation -0.350 -0.216 0.183 0.056mdummy -0.136 -0.014 -0.178 0.221mbigdummy -0.036 -0.026 -0.119 0.160
We can construct many kinds of models by selecting a various combination of expressions.
For example the richest model is made up of all expressions used in monthly reports. However,
we find that such less parsimonious models tend to yield a larger number of topics, sometimes
more than 15, which prevents us from drawing useful implications. At the same time, we
confirm that the above results hold in many models. One interesting note is that “etc” is often
20
used in tandem with negative expressions.
2.6 Findings So Far
In this empirical part of the study, we find mainly two things. First, the Bank of Japan has a
forecasting power for the economy. Positive-negative indicators complied from monthly reports
lead the leading index by three months. This implies that the Bank of Japan has some superior
information to private agents, which serves as one of our modeling bases in the next section.
The second concerns the characteristics of the Bank of Japan’s communications. We find
that ambiguity tends to increase (decrease) when the economy is bad (good). More specifi-
cally, word length tends to be longer and modality and “etc” expressions tend to appear more
frequently when the leading index is low. The LDA suggests that modality is used in tandem
with negative expressions when the leading index is low.
3 Game-Theoretic Model
In this section we develop a simple game-thoretic model to understand the empirical findings
we have presented so far. We model communication by a central bank as a persuasion game
or verifiable disclosure model (e.g. Milgom, 1981; Shin, 2003), where the sender can choose to
disclose or withhold private information to the receiver, but cannot fabricate it. The assumption
is in contrast to that in “cheap talk” models (e.g. Stein, 1991), where the sender is allowed to
send any kind of message costlessly no matter what the private information is. The assumption
that the sender (central bank) cannot lie but can withhold information is relevant to the context
of the central bank’s periodic reports, because the data they contain may be verified later, and
because repeated interaction with the receiver/market (which is not modeled explicitly here)
means that there may be significant reputational and political costs if the central bank is found
to have fabricated information. Meanwhile it would be much more difficult for the market to
discern whether the central bank did or did not have a certain piece of information, as assumed
in our model below.
3.1 Setup
The economy consists of a central bank (CB) and a representative market participant (P).
The CB is the sender of information and the P is the receiver. There are three states of the
macroeconomy y ∈ {−1, 0, 1}. Each state arises with strictly positive probability and is either
partially or completely known to the CB but unknown to the P, as we will describe in detail
21
shortly. The feature that the CB has private information is consistent with our finding that
the Bank of Japan has forecasting power for the economy.
The P’s payoff is given by a quadratic loss function −(y−V )2. The CB’s report is denoted
by m, and the P Bayesian-updates the belief about the economy based on m and best responds
so that the P’s reaction is given by V ∗ = E[y | m].
We assume that the P’s interest and that of the CB are not completely aligned in the sense
that, conditional on the state y, the CB wants the P to take a higher action than y. In the
Appendix, we present a simple foundation of the CB’s bias and demonstrate that the CB is
upward biased when the inflation rate is lower than the target and downward biased otherwise.9
In this paper, we adopt such an assumption that the CB has an upward bias, since during the
data period Japan has seen deflation or inflation lower than the current target level of two
percent. For simplicity the CB’s payoff function is given by V .10 This implies that the CB is
better off when the market reaction is higher.
Before publishing the report m, the CB receives two types of private signals about the state
of the macroeconomy, namely S ∈ {SL, SH} and s ∈ {−1, 0, 1}. The CB receives an ambiguous
signal S with probability 1. If S = SL then y ∈ {−1, 0}, that is, y may be low. If S = SH ,
then y ∈ {0, 1}, that is, y may be high. In addition, the CB receives a more detailed, clear
signal s with probability θ ∈ (0, 1). The clear signal is perfectly informative about the state: if
s = x then y = x. The parameter θ is common knowledge and represents how well the CB is
informed. The CB’s choice in this game is which signal to disclose or withhold.
3.2 Equilibrium
Let us consider how information is revealed in a perfect Bayesian equilibrium of this game. The
first step is to see that in equilibrium the CB cannot completely withhold private information.
Suppose that the CB does not publish any information. Then the P’s reaction will be V = E[y],
where E[y] is the unconditional expectation of y. However, when S = SH , the CB reveals the
signal since it induces a higher reaction E[y | SH ] > E[y]. In turn, if the CB does not reveal
S = SH or s, then the P can infer that S = SL (recall the assumption that the CB always
receives S). The P is indifferent between publishing S = SL and not publishing any information,
and in any case S is perfectly revealed in equilibrium. Naturally, when s is not observed, the
CB only publishes S ∈ {SL, SH}.When the CB observes a clear signal s, there are four cases.
First, if s = −1, the CB withholds s = −1 and publishes only S = SL. This is because
9Alternatively, see e.g. Walsh [Chapter 7, 2010] for discussions on such inflation bias.10This particular form of the payoff function is not essential. Our results hold, for example, if the CB’s payoff
function is −(y + b− V )2 and b is large enough, where b > 0 is the CB’s upward bias.
22
we have V = E[y | m = SL] > −1, which holds since the P cannot tell whether the CB has
received s = −1 and withheld it or has not received s and the state y can be either −1 or 0.
If s = 0 and S = SL, the CB reveals s = 0, since it induces higher reaction V = 0 > E[y |m = SL].
If s = 0 and S = SH , the CB withholds s = 0, since V = E[y | m = SH ] > 0.
Finally, if s = 1, the CB reveals s = 1, since V = 1 > E[y | m = SH ].
The above arguments can be summarized in the following proposition.
Proposition 1 In the unique perfect Bayesian equilibrium,
i) if clear signal s is not observed, then the CB’s report is ambiguous;
ii) if s is observed, then the CB sends the report of m = s only when s = 1 or when s = 0
and S = SL.
The proposition has the simple intuition that, because of the upward bias, the CB hides
a clear signal whenever the corresponding ambiguous signal induces a more optimistic belief
(amd reaction). The results can be related readily to our empirical findings.
Remark 2 The negative reports are always ambiguous.
The CB never reports s = −1. If s = −1, then the CB hides it and sends an ambiguous
report m = SL instead. In the context of our empirical analysis, m = SL can be thought of as
reporting negative sentences with modalities, which makes them ambiguous and less categorial
about the state of the economy. The market cannot know for certain whether modalities are
used because the CB does not have clear information, or because the CB has clear information
but withholds it to influence the beliefs in the market. Positive reports can also be ambiguous
(m = SH) but if a clear signal has been obtained, it is revealed, which suggests that positive
expressions in the monthly reports are less likely to contain modality.
In addition, the model generates further implications beyond our empirical findings.
Remark 3 If taken literally, the CB’s reports are upward biased.
If s = −1, then m = SL. If s = 0 and SH is observed, then m = SH . Although the P rationally
Bayesian-updates and thus is never deceived, the expressions in the monthly reports should
include more positive and less negative expressions than the state of the economy indicates.
Remark 4 Let m ∈ {SL,−1} be an pessimistic report and m ∈ {SH , 1} be an optimistic report.
Then on average, optimistic reports have more impact on the market reaction than pessimistic
reports.
23
The apparent asymmetric reaction of the market is only due to the fact that m = −1 is never
revealed and thus the overall reaction is dampened when the report is negative. This can be an
explanation for the finding by Born, Ehrmann, and Fratzscher (2014) that financial stability
reports from the central banks around the world lead to significant positive stock market returns
when they are optimistic, but no such effect is found when they are pessimistic.
4 Concluding Remarks
TBA
References
[1] Apel, M. and M. Blix Grimaldi (2012) “The Information Content of Central Bank Min-
utes,.” Sveriges Riksbank Working Paper Series 261.
[2] Blinder, Alan S. (2004) “The Quiet Revolution: Central Banking Goes Modern.” New
Haven: Yale University Press.
[3] Born, Benjamin, Michael Ehrmann, and Marcel Fratzscher (2014) “Central Bank Com-
munication on Financial Stability.” Economic Journal, 124, pp. 701–734.
[4] Boukus, E. and J. V. Rosenberg (2006) “The Information Content of FOMC Minutes.”
mimeo, Federal Reserve Bank of New York.
[5] Crawford and Sobel (Etrica);
[6] Eijffinger, Sylvester and Petra Geraats (2006) “How Transparent are Central Banks?”
European Journal of Political Economy, 22, pp. 1–21.
[7] Fairclough (1989)
[8] Gramsci, Antonio (1971) “Selections from the Prison Notebooks of Antonio Gramsci.”
New York: International Publisher.
[9] Hendry, S. (2012) “Central Bank Communication or the Media’s Interpretation: What
Moves Markets?” Bank of Canada Working Papers 12-9.
[10] Hendry, S., and A. Madeley (2010) “Text Mining and the Information Content of Bank of
Canada Communications.” Bank of Canada Working Papers 10-31.
[11] Hansen, Stephen, Michael McMahon, and Andrea Prat (2014) “Transparency and Delib-
eration within the FOMC: a Computational Linguistics Approach.” mimeo.
24
[12] Inui and Okazaki (XXX)
[13] Milgrom, Paul (1981) “Good News, Bad News: Representation Theorems and Applica-
tions.” Bell Journal of Economics, 12, pp. 380–391.
[14] Narrog, Heiko (2009) “Modality in Japanese: The Layered Structure of the Clause and
Hierarchies of Functional Categories.” Amsterdam: Hohn Benjamins Publishing.
[15] Palmer, F. R. (2001) “Mood and Modality.” Second Edition. Cambridge: Cambridge
University Press.
[16] van Dijk (2008)
[17] Shin (2003)
[18] Stein (1989 AER);
[19] Walsh, Carl E. (2010) “Monetary Theory and Policy.” Cambridge: MIT Press.
[20] Woodford, Michael (2003) “Interest and Prices.” Princeton: Princeton University Press.
25
A Japanese-English Correspondence Tables
In each cell, the left expression indicates the original Japanese morpheme corresponding to the
right English expression that we translated for this paper.
Table 10: Top Ten Polarity Expressions
Positiveexperience evaluation
juyou demand ryouko goodkaizen improve tame good/reasonshikin fund yawaragu ease
kaifuku recovery juntaku amplekeiki economy takamaru grow
shotoku income kousuijyun high levelshuueki profit meikaku clear
shikin juyou credit demand medatsu conspicuouskanousei possibility kenchou firmjyukyuu supply and sekkyoku active
demand conditions
Negative Evenexperience evaluation
geraku fall kajou excess toshi investteika decline yowai weak zoka increase
kakaku price mainasu minus kankyou environmentakka worsen teichou sluggish yuruyaka modest
kosuto cost toboshii weak seisan produceshikin guri financial positions fukakujitsu uncertain kanwa ease
donka subjue kanman lackluster naka under/middlekibishii severe futoumei uncetain suijun levelteimei weak zeijaku fragile kichou trendrisuku risk - - koyou employment
26
Table 11: Modality Expressions
high risk u seem, appearga ukagawareru seem, appearkoto ga mikomareru expectedkoto wa tenbou shinikui unlikelyte iku koto ga kitai sareru expectedte iku to mirareru seem, appearte iku to yoso sareru forecastedte iku to kangae rareru consideredte iku mono to kitai sareru expectedte iku mono to kangae rareru consideredte iku kanousei ga takai likelyte iru to mirareru seem, appearte iru mono to mirareru seem, appearte iru yo ni ukagawareru seem, appearde iku to mirareru seem, appearde iku to kitai sareru expectedto mirareru seem, appearto yosou sareru forecastedto kangae rareru consideredto mikomareru seem, appearwa izen toshite kitai shinikui jokyo ni aru still difficult to expectmo ukagawareteiru seem, appearreteiru to kangae rareru consideredwo tadoru tono mikata ga ippanteki de aru generally thoughtkousan ga ookii likelykanousei ga ookii likelyhajimeru to kangae rareru consideredtsudukete iku to mirareru seem, appear
low risk risuku niwa hikitsuzuki ryuui ga hitsuyou de aru attention should still be paid to the possibility ofkanousei ga aru maykanousei nimo ryuui ga hitsuyou de aru warrant careful monitoring
unreal ga hitsuyou de aru shouldte iku koto ga hitsuyou de aru shouldte iku koto ga jyuuyou to kangae rareru is important tote iku koto mo jyuuyou to kangae rareru is important tote iku hitsuyou ga aru shouldte mite iku hitsuyou ga aru should be observedhitsuyou ga aru should
27
Table 12: Other Miscellaneous Expressions
Negative conjunctions ga, mo, monono, nao, nagara, but, althoughshikashi, tadashi, mottomo, tada but, althoughtsutsu, ippou while
Clear adverbs hakkiri clearkiwamete extremekanari very, quite
Ambiguous adverbs yaya, ikubun, wazuka, jakkan slight, a bitoomune, soujite, zentai generaljojoni gradualhobo, zengo, aru teido almost, abouttoumen for the time beingichibu parthoka etc, and so on
Ambiguous nouns keiko, kichou tendkennnai more or less
Etc nado, tou etc, and so on
B Monetary Policy Change Dummy
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Table 13: Monetary Policy Change Dummy: Otherwise dummies are zero.
Monetary policy Big changechange dummy dummy Notes
1998.09 -1 -1 call rate from 0.5% to 0.25%1999.02 -1 -1 call rate to 0.15%2000.08 1 1 call rate to 0.25%2001.02 -1 -1 call rate to 0.15%2001.03 -1 -1 quantitative easing (5 tril yen)2001.08 -1 0 6 tril yen2001.09 -1 0 over 6 tril yen2001.12 -1 0 10 to 15 tril yen2002.02 -1 0 increase the purchase of long-term bonds ( 0.8 to 1 tril yen/month)2002.10 -1 0 increase the purchase of long-term bonds ( 1 to 1.2 tril yen/month)2003.03 -1 0 17 to 22 tril yen2003.04 -1 0 22 to 27 tril yen2003.05 -1 0 27 to 30 tril yen2003.10 -1 -1 27 to 32 tril yen; enhance monetary policy transparency2004.01 -1 0 30 to 35 tril yen2006.03 1 1 terminate quantitative easing; understanding of price stability2006.07 1 1 call rate from 0% to 0.25%2007.02 1 1 call rate to 0.5%2008.09 -1 0 U.S. dollar funds-supplying operation2008.10 -1 -1 call rate to 0.3%2008.12 -1 -1 call rate to 0.1%; purchase or long-term bonds (1.2 to 1.4 tril yen/month)2009.03 -1 0 purchase or long-term bonds (1.4 to 1.8 tril yen/month)2009.12 -1 -1 enhance easy monetary conditions; clarify price stability2010.04 -1 0 strengthen the foundations for economic growth2010.10 -1 -1 comprehensive monetary easing; call rate 0 to 0.1%; asset purchase program2011.03 -1 0 asset purchase program to 40 tril yen2011.08 -1 0 asset purchase program to 50 tril yen2011.10 -1 0 asset purchase program to 55 tril yen2012.02 -1 0 asset purchase program to 65 tril yen2012.04 -1 0 asset purchase program to 70 tril yen2012.09 -1 0 asset purchase program to 80 tril yen2012.10 -1 0 asset purchase program to 91 tril yen2012.12 -1 0 asset purchase program to 101 tril yen2013.01 -1 -1 2% inflation target; accord with the government2013.04 -1 -1 Quantitative Qualitative Monetary Easing (QQE)2014.10 -1 -1 expand QQE
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C Model of a Central Bank’s Incentive
In this appendix, we provide a model example in which the CB has an upward bias based on
a standard New Keynesian model.
The CB’s loss function is given by
LCB = π2 + λ(π − πe)2, (1)
where π and πe represent the inflation rate and the inflation expectations, respectively. The
first term indicates an inflation aversion as is commonly used in the New Keynesian model
such as Walsh (2010) and Woodford (2003). To this, we add the second term that indicates
an aversion to inflation surprises. Rationales for this assumption is that surprises increase
uncertainty, and in turn, dampen economic activity. Or, the P may simply dislike uncertainty,
as is assumed below, and a part of the P’s utility constitutes the CB’s utility. A parameter λ
represents a weight on inflation surprises.
The P’s loss function is expressed as
LP = π2 + γ(π − πe)2, (2)
where γ represents a weight. A divergence of preferences arises between the CB and the P
unless λ = γ.
The standard New Keynesian Phillips curve is assumed as
π = κy + βπe, (3)
where y represents an output deviation from its natural level and κ and 0 < β < 1 represent
parameters.
The model proceeds as follows. First, the CB sends information on y, y∗, after observing
true y. Second, the P receives y∗ and forms expectations for y, and in turn, π, as
πe =κ
1− βye. (4)
This is derived from the the rational expectation solution of equation (3), where πe = E(π|y∗)and ye = E(y|y∗).
Then, the CB’s loss function becomes
LCB =
{κy +
κβ
1− βye}2
+ λκ(y − ye)2 (5)
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This equation suggests that, when y < 0, the CB has an incentive to achieve ye > y. In
other words, the CB wants the P to be optimistic about y. To see this, first consider a case
where λ is zero. In this case, the CB’s loss is minimum when ye = −1−βκβ y, which is positive
and larger than y. If the P thinks that y is positive, it raises its inflation expectation, which
in turn, contributes to the stabilization of the inflation rate around zero due to equation (3).
This helps lower the CB’s loss. Second, consider a case where λ is infinite. In this case, the
CB’s loss is the lowest when ye = y. In an intermediate region of λ, the optimal ye for the CB
lies between −1−βκβ y and y, which is below y.
In other words, when the economy is bad (y < 0), the CB has an upward bias. The CB
has an incentive to send better information than fundamentals to prevent deflation. In Japan,
the inflation rate has been below the two percent target for two decades. In this respect, the
assumption of y < 0 seems plausible.
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