Re-Evaluation of Japan’s Monetary Policy in the late 1980s
with the Interest Rate Gap 1
Shingo Umino 2
Graduate School of Economics, Kobe University
Graduate School of Economics Kobe University Working Paper Series No.301

1Introduction

It is readily apparent that a macroeconomic model that enables forecasting of aneconomy and analyses of a policy’s impact has developed, although it has not reached amaximum point. Naturally, considering future policy action, it is important to obtain anevaluation of past policy through empirical works based upon historical data. However,
now that it is possible to use a dynamic stochastic general equilibrium (DSGE) model
describing a national economy, then using Bayesian techniques, the Bayesian-DSGE(B-DSGE) model, which is a point connecting theory with reality, is available for us. As

1

I would like to thank Shigeto Kitano for valuable insights and comments. All errors are my own responsibility.

2E-mail:shingo.umino@gmail.com

described in this paper, a goal is to reevaluate monetary policy in the late 1980s using thelatent variables from the B-DSGE model.
The conclusion reported by Jinushi, Kuroki, and Miyao (2000) is reconsidered: that thetightening monetary policy in 1987–88 was delayed. This paper presents examination ofwhether the delay of monetary policy included actual tightening by interpreting the naturalrate of interest (NRI) measured from a B-DSGE model.
A Bayesian likelihood approach has been used to estimate a fully specified DSGE
model since Smets and Wouter (2003). The DSGE model in this paper resembles thatwhich excludes the investment-specific technological progress from the Hirose andKurozumi (2012) model. Sugo and Ueda (2008) and Fujiwara et al. (2011) also estimate thelarge-scale DSGE model with Bayesian techniques and Japan’s aggregate time series data.
This paper shows no evidence of the delay of monetary policy in the late 1980s, using
the interest rate gap. Rather, because the BOJ did not specifically examine its own economy
precisely, apparently no one has noticed that the stance of monetary policy has showntightening since 1985.

2Linearized DSGE ModelA DSGE model here mostly follows that of Hirose and Kurozumi (2012). I present thelinearized DSGE model estimated in the next section. The dynamics of consumption, which
follows from the consumption Euler equation with respect to consumption andbond-holding, is given ascˆt =−βθ 1 θ βθ
1 θ1  βθ 111
 E t cˆt +1 − αcˆt −1 +E t cˆt + 2++Ω2Ω3 zΩ 3  z α Ω1Ω 2 Ω 2 z α Ω1Ω 2 z z Ω1Ω 2 Ω 3

1 ˆ( Rt − E t πˆ t +1 − rt N )σ(1)where ĉt denotes consumption, R̂t is the nominal interest rate, and πˆ t stands for theinflation rate: Ω1 ≡ 1 − θ/z , Ω 2 ≡ 1 − βθ/z α , Ω 3 ≡ 1/Ω 2 + βθ/z α (1/Ω1Ω 2 )(θ/z ) + 1/Ω 2 (θ/z ) . Then thenatural rate of interest r̂t N is defined as the following.
rˆt N ≡ −+σθΩ 2Ω3 zz tz +σ  βθ
βθσ1 θ
Et z tz+ 2++ 1 Et z tz+1 − αΩΩΩΩ 3  z α Ω 2 zz
1 23
βθ 1
11  βθz tb −+ 1 Et z tb+1 + α
Et z tb+ 2 .
Ω2Ω3Ω2Ω3  zαΩΩz23Therein, ztb represents a preference shock.
Labor supplied by households is differentiated by a union, so that some monopoly
power over wages exists. Such backgrounds result in an explicit wage equation and allow
for the introduction of Calvo-style sticky nominal wages. Because of nominal wage
stickiness and partial indexation of wages to inflation 3, real wages adjust only gradually to3

See Hirose and Kurozumi (2012).

(2)

the desired wage aswˆ t − wˆ t −1 + πˆ t − γ wπˆ t −1 + z tz = βz 1−σ ( E t wˆ t +1 − wˆ t + E t πˆ t +1 − γ wπˆ t + E t z tz+1 )+1 − ξ w (1 − βξ w z 1−σ )λ w ˆ ˆ( χl t − λt − wˆ t + z tb ) + z tw ,
ξ w λ w + χ (1 + λ w )(3)where ŵt represents the real wage, lˆt denotes labor, λ̂t stands for the marginal utility of
consumption, and z tw is a labor shock.
Aggregating the Cobb–Douglas production functions over intermediate good firms
generatesyˆ t = (1 + φ ){(1 − α )lˆt + α (uˆ t + kˆt −1 − z tz )},where k̂ t represents capital, û t is the utilization rate of capital, and z tz is a technology
shock.
Each intermediate good firm produces a differentiated good of one kind by choosing apair of capital and labor to minimize production cost. Because of Calvo-type pricestickiness and partial indexation to lagged inflation of those prices that can not bereoptimized, as described in Smets and Wouters (2003), prices adjust sluggishly to their
desired price. Profit maximization by price-setting firms gives rise to the following New
Keynesian Phillips curve as(4)πˆ t − γ p πˆ t −1 = βz 1−σ ( Et πˆ t +1 − γ p πˆ t ) +(1 − ξ p )(1 − βξ p z 1−σ )ξpmˆ ct + z tp ,(5)

where m̂ct denotes the real marginal cost and z tp represents a cost-push shock.
As in Hirose and Kurozumi (2012), the capital service firm owns the entire stock ofcapital at the beginning of each period. It rents utilization-adjusted capital to intermediate
goods firms. The optimality condition for profit maximization with respect to investmentiˆt yields

βz 1−σ1 ˆ ˆ{it − it −1 + z tz + z ti } = qt +{E t iˆt +1 − iˆt + E t z tz+1 + E t z ti+1 },

ζζ

(6)

where z ti is an investment shock.
The monetary authorities follow a generalized Taylor rule by gradually adjusting thepolicy-controlled interest rate.
φRˆ t = φ r Rˆ t −1 + (1 − φ r ) π 43∑
j =1πˆ t − j + φ y ( yˆ t − yˆ t* ) + z tr
In that equation, yˆ t* is the potential output and z tr is a monetary policy shock.
Equations (1)–(7) and just six equations––the capital accumulation, utilization rate,
Tobin’s q, real marginal cost, cost-minimizing condition, production function, and potential
output––determine 13 endogenous variables. All seven persistent shocks z tz , z tb , z tw , z tg , z ti , z tp ,(7)

and z tr follow the stationary first-order autoregressive processes 4.

3Bayesian estimation

Our estimation methodology resembles that of Smets and Wouters (2003). We use
Dynare, which is a useful tool for conducting Bayesian estimation 5. The Dynare toolbox
derives the reduced-form representation of the DSGE model and automatically provides
stability and eigenvalue analysis. Moreover, it enables us to conduct Bayesian estimation 6.
The details of prior distributions used in the paper are presented in Table 1. Choosing
the prior distributions, we mostly follow the previous literature on the Japan’s Bayesianestimation: Hirose and Kurozumi (2012). The last two columns in Table 1 report theposterior for the structural parameters.
The data used for estimation are the real output growth rate, the real consumption
growth rate, real investment growth rate, real wage growth rate, hours worked, inflation
rate, and nominal interest rate in Japan’s economy 7. Similar to Hirose and Kurozumi (2012)

4z tg is an expenditure shock.

5

The Dynare file is available from the author upon request.

6

See An and Schorfheide (2007) for a detailed explanation of Bayesian estimation.

7

See Hirose and Kurozumi (2012). However, different Hirose and Kurozumi, the estimated potential output was not used herein.

and previous studies, all data were obtained at quarterly frequencies of 1980Q2 to 1991Q4.
The end of the sample is determined to exclude the period of the zero nominal interest rate
policy because this paper specifically examines monetary policy before the bubble burst.

4Implications

Jinushi, Kuroki, and Miyao (2000) presumed that the average economy during
1975–85 was ideal and that the benchmark nominal policy rate arose from an ad-hoc policy
rule. Furthermore, they examined the difference between the actual policy rate and therule-based one. As described herein, unlike Jinushi, Kuroki, and Miyao (2000), who
considered a desired economy existing every period by the B-DSGE model, this paper
presents a specific examination of the gap separating the natural rate of interest and theactual policy rate.
Figure 1 depicts the interest rate gap defined as the difference in between the nominal
Call rate and the nominal based NRI 8. The shaded areas indicating recessions are dated by
the Cabinet Office.

8From the Fisher equation, the nominal based natural rate of interest here is equal to the posterior mean of the two-sided smoothed

estimate of the natural rate of interest adding the model-based expected inflation

E t π t +1 .

The monetary policy in 1987–88, broadly speaking, was in a position where theeconomy was unexpectedly tightened. After the Plaza Accord of 1985, the rapid
appreciation of the yen brought about recessionary effects, and affecting the decline of thepotential output, the natural rate of interest fell. Therefore, the positive gap has remained.
Moreover, as in Table 1, whereas the policy response to the output gap was quite low( φ y = 0.069 ), the authority preferred the stable inflation ( φπ = 1.697 ) and the indexation to thepast policy rate ( φ r = 0.458 ). The salient implication is that the BOJ had not misunderstood
the potential economy and that neither economic agent noticed it. Consequently, no delayof monetary policy tightening is evident 9. Instead, it seems clear that the monetary policy
stance was potentially tightening.
Because the BOJ would not raise the controlled policy rate, the discount rate, in 1987
immediately and dynamically, by tightening monetary policy gradually, promoting theaccumulation of bubble can not be ruled out. However, it is interpreted that the tightening
policy had maintained upward growth since the big tightening in 1988Q1.

9Figure 1 also shows that the easy monetary policy in the early 1980s used to ride out the recession caused by the second oil crisis in1979 indicates negative gaps.

5Conclusion

As described in this paper, the aim is to evaluate monetary policy of the late 1980s. As
a consequence, the latent interest rate gap that Jinushi, Kuroki, and Miyao (2000) did not
address showed that no monetary policy at that time was delayed. Rather, the authority
could not ascertain the potential condition of the economy. Monetary policy wasunexpectedly on the tightening side from 1985.

References

An, Sungbae, and Frank Schorfheide (2007) Bayesian Analysis of DSGE Model,Econometric Reviews, 26(2-4), 113–172.
Fujiwara, Ippei, Yasuo Hirose, and Mototsugu Shintani (2011) Can News Be a Major
Source of Aggregate Fluctuations? A Bayesian DSGE Approach, Journal of Money,Credit and Banking, 43(1), 1–29, 02.
Hirose, Yasuo and Takushi Kurozumi (2012) Do Investment-Specific Technological
Changes Matter For Business Fluctuations? Evidence from Japan, Pacific Economic
Review, 17(2), 208–30, 05.

Jinushi, Toshiki, Yoshihiro Kuroki, and Ryuzo Miyao (2000) Monetary Policy in Japan
Since the Late 1980s: Delayed Policy Actions and Some Explanations, in Ryoichi
Mikitani and Adam Posen eds. Japan’s Financial Crisis and Its Parallels to U.S.
Experience, Washington D.C.: Institute of International Economics.
Smets, Frank, and Raf Wouters (2003) An Estimated Dynamic Stochastic General
Equilibrium Model of the Euro Area, Journal of the European Economic
Association, 1(5), 1123–75, 09.
Sugo, Tomohiro, and Kozo Ueda (2008) Estimating a Dynamic General Equilibrium Modelfor Japan, Journal of the Japanese and International Economics, 22(4), 476–502.

Table 1: Prior and posterior distributions of parameters
Prior distribution
Parametersσθχ
1/ζφγw

ξw
γpξp
λp

φr

φπ

φyρz

Posterior distribution

Descriptions

Distribution

Mean

Mean

90% intervalrisk aversion
habit
consumption
elasticity oflabor supply
elasticity of
invst. adj. costs.s. fixed cost
wage
indexationa fraction ofwage

Gamma
Beta

1.000
0.700

2.676
0.146

[2.017, 3.293][0.070, 0.218]

Gamma

2.000

3.848

[2.347, 5.320]

Gamma

4.000

4.276

[1.638, 6.800]

Gamma
Gamma

0.075
0.500

0.072
0.385

[0.053, 0.091][0.014, 0.732]

Gamma

0.375

0.109

[0.038, 0.176]

Gamma

0.500

0.211

[0.001, 0.432]

Gamma

0.375

0.697

[0.616, 0.773]

Gamma

0.150

0.219

[0.109, 0.331]

Gamma

0.800

0.458

[0.282, 0.645]

Gamma

1.700

1.697

[1.540, 1.854]

Gamma

0.125

0.069

[0.024, 0.113]

Beat

0.500

0.074

[0.015, 0.128]

price
indexationa fraction of
prices.s. pricemarkupinterest ratesmoothingpolicyresponse of
inflation
policyresponse of
outputpersistence of

ρb

ρi
ρg

ρw
ρp

ρr

σzσb

σiσgσwσpσr

technology
persistence ofpreference
persistence ofinvestment
persistence ofextendeddemandpersistence ofwage markup
persistence of
price markup
persistence ofmonetary
policyst. dev. oftechnology
st. dev. ofpreference
st. dev. ofinvestment
st. dev. ofextendeddemandst. dev. ofwage markup
st. dev. of
price markup
st. dev. ofmonetary
policy

Beat

0.500

0.845

[0.757, 0.929]

Beat

0.500

0.606

[0.395, 0.869]

Beat

0.500

0.936

[0.902, 0.971]

Beat0.500

0.350[0.081, 0.603]

Beat

0.500

0.835

[0.719, 0.958]

Beat

0.500

0.822

[0.715, 0.926]

Inversegamma
Inversegamma
Inverse
gamma

0.500

1.815

[1.455, 2.165]

0.500

0.221

[0.128, 0.310]

0.500

5.401

[2.738, 8.438]

Inverse
gamma

0.500

2.227

[1.807, 2.637]

Inversegamma
Inversegamma
Inverse
gamma

0.500

1.377

[0.967, 1.782]

0.500

0.277

[0.155, 0.392]

0.500

0.178

[0.144, 0.212]

Note: The paper follows the setting of prior distributions by Hirose and Kurozumi (2012).Figure 1: Interest rate gap.