論 文 の 要 約

氏名中西

論文題目正An Empirical Study of Japanese Monetary Policy Using Non-Gaussian
SVAR Model
非ガウス型 SVAR モデル による日本の金融政策の実証研究

Abstract
Chapter 1
The purpose of this study is to analyze the effects of the so-called
unconventional qualitative and quantitative monetary easing (QQE) policy that theBank of Japan (BOJ) launched in April 2013 to overcome the long-term deflation inJapan. For this policy BOJ adopted the monetary base as a policy variable. Since
the introduction of QQE, the MB has continued to rise rapidly, from about 49 trillion
yen in April 2013 to about 651 trillion yen in May 2021. However, the inflation
target of a stable 2 percent year-on-year increase in consumer prices by the BOJ
has not been achieved on a sustainable basis. A review of actual data shows thatalthough the inflation target was temporarily achieved at times, the effect was not
as great as initially expected. In addition, since the introduction of QQE, there havebeen a number of negative effects, that is, the environment surrounding theJapanese economy has undergone major changes, including the expansion of QQEwith negative interest rates, two consumption tax hikes, and COVID-19. Given
these factors, we decided to analyze the impact of the increase in the MB using thestructural vector autoregressive (SVAR) model.
Econometric methods are used to analyze monetary policy. Traditionally,
the distribution of the structural error term has been assumed to be Gaussian.
However, in analyses using actual data, the structural error terms are often nonGaussian. Therefore, we proposed a new analysis method, which we call it “flexiblequasi-log likelihood function approach” that assumes that the structural error term
is non-Gaussian, and verify the effectiveness of the method by a Monte Carloexperiment. On the other hand, there already exists a R program called ‘id.ngml’
for NG-SVAR model analysis assuming a t-distribution. We also use id.ngml forcomparison to our new method. Since both ‘id.ngml’ and our method use the ‘id.ngml’
Independent Component Analysis ICA we first briefly introduce ICA and associatedalgorithm called ‘fastICA’ and then explain how to apply ICA for NG-SVAR model1

analysis.
Chapter 2
In this chapter, we analyzed the effects of Japan's monetary policy in three
periods: before the introduction of QQE, after the introduction of QQE, and for thewhole period including these periods. Moreover, we reviewed Japan's monetary
policy to date and provides a survey of studies that have conducted empiricalanalyses of monetary policy. We introduced the accuracy of the estimation using thequasi-log-likelihood function of the t-distribution, referring to Maekawa (2021). We
construct 5-variable models and summarize the results of each estimation based on
the impulse response functions (IRF). We extend the 5-variable models to take into
account the impact of the consumption tax hike and conduct an analysis.
In this chapter, following Miyao (2016), we estimated the 5-variable models
that employed stock or exchange rates. In addition, the 6-variable models wereestimated by adding a consumption tax variable to the previous 5-variable models.
The effect of the increase in the MB of the policy variables was not enough to achieve
the 2 percent inflation target set by the BoJ. As a result of our analysis, we cannot
deny the possibility that even with unconventional monetary policy, the economy
fell into a liquidity trap. Comparing the 5-variable model with the 6-variable model,
the results of the 6-variable model analysis show that the impact of the increase in
the MB is small and the impact of the consumption tax hike on the real economy islarge.
We conducted our analysis assuming that the structural errors are a 𝑡distribution. However, there remains the problem because t-distribution should beused. We had better to search a better p.d.f from a wider range of probability
distributions family without assuming the t-distribution. We consider this problem
in the next chapter.
Chapter 3
In this chapter we propose a new method of estimating NG-SVAR model
named "flexible quasi-log likelihood function approach to calculate the quasimaximum likelihood estimator (say, Flexible q-MLE)" and check the performanceof it by Monte Carlo Experiments. In the experiments we generated the structural
errors in NG-SVAR mode by the independent and identically distributed random
error terms from the t-, Laplace-, and Hyperbolic Secant-distributions. Thenassuming that we do not know this error distribution and estimate these errors.
Next using estimated errors, we estimate the p.d.f of the structural errors. Then weadopt the estimated p.d.f as the approximate p.d.f of the structural errors. Finally,
we construct the approximate log-likelihood function and estimate the target
parameters in the NG-SVAR model.
To evaluate the performance of our estimator, we conducted Monte Carlo2

experiments under the DGP, assuming that the structural error terms of the SVARmodel follow three non-Gaussian distributions, namely, t-, Laplace-, and hyperbolic
secant-distributions. For these three non-Gaussian distributions, we searched forthe most suitable pdf from the Pearson family using the ‘pearsonMSC’ R function.
Afterwards, we constructed the quasi-log-likelihood function using the selected pdf
and calculated the quasi-log-likelihood estimator. Based on the experimental
results, the quasi-log-likelihood estimator showed satisfactory performance. Finally,
we applied our proposed method to an NG-SVAR model for the US economy withfive macroeconomic variables to assess the effect of quantitative easing monetary
policies, finding no serial correlations between structural and reduced-form errors.
Although uncorrelatedness does not necessarily mean independence, weinterpreted it as a weak measure of independence and proceeded with the analysis
as customary.
Chapter 4
In this chapter, instead of assuming the distribution of the structural error
term to be the t-distribution as in Chapter 2, we find a distribution that fits thedata better from the Pearson distribution and adopt it as the approximatedistribution of the structural error term. Using the approximate distribution sochosen, we construct the pseudo-likelihood function and calculate the maximum
pseudo-maximum likelihood estimator of the coefficient matrix of the structural
VAR model. The IRF is further estimated based on the results. We note that thescale and sing of IRF cannot be determined in ICA and its algorithm fastICA. This
indeterminacy is inevitable in theory. Eventually, the only way to overcome thisproblem is to compare it with the actual data, to use the exogenous informationsuch as economic theory and/or empirical knowledge.
In this chapter we used the IRF calculated from ‘id.ngml’, which assumes
a t-distribution, as a reference to determine the sign using economic theory and to
determine the effect of monetary policy. One standard deviation shock of the IRF is
calculated from the data, the standard deviation of the MB in the after-period model
is about 138.4 trillion yen, and the standard deviation of the MB in the whole-period
model is about 169.9 trillion yen. From the IRFs, an increase in MB has a positiveeffect on Gross Domestic Product (GDP), although the degree of the effect varies. Infact, the GDP growth rate has continued to grow, albeit at a low rate. In addition,
the rise in stock and the impact of the weaker yen on the exchange rate areconsistent with actual economic conditions. In summary, the theoretical consistencyof monetary policy is confirmed. However, although positive effects on inflation havebeen confirmed in some models, it is difficult to say that the effects have been
sufficient to achieve the 2% inflation target set by the BoJ.

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Chapter 5
Since all chapters are already summarized, we show here much shorter summary
as follow.
Chap.2: We compared the IRFs of the 5-variable with stock variable data and 6variable with stock variable and consumption tax data models. After considering
the effect of the consumption tax hike and conducting the analysis, we found thatthe effect of the increase in MB is smaller and the effect of the consumption tax hikeis larger.
Chap.3: The results of Monte Carlo experiments show that the pseudo-maximum
likelihood method proposed in this paper yields better estimation results.
Chap.4: The IRF obtained both from our flexible quasi-log likelihood function
method and from ‘id.ngml’ have a problem that the sign and scale are not uniquelydetermined. However, the IRF modified using economic theory is very similar to theIRF of ‘id.ngml’, indicating that the empirical analysis using the flexible pseudo-log
likelihood function method is an effective analytical method.
Finally, we will mention future research. Although our quasi-maximum loglikelihood estimator showed satisfactory performance in both Monte Carloexperiences and empirical studies, little is mentioned the rationale in thisdissertation. However, it seems possible to give the rationale asymptotically along
the line of Lanne et al (2017). We will extend this paper towards this direction.
Another direction of future research is to extend is to apply this research to a panel
SVAR model. We will also use the panel NG-SVAR model to analyze the spillover
effects of U.S. tightening monetary policy on emergent countries.

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