Large Shocks and the Business Cycle: The Effect of Outlier Adjustments
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This study examines the impact of outlier-adjusted data on business cycle inferences using coincident indicators of the composite index (CI) in Japan. To estimate the CI and business cycles, this study proposes a Markov switching dynamic factor model incorporating Student’s t-distribution in both the idiosyncratic noise and the factor equation. Furthermore, the model includes a stochastic volatility process to identify whether a large shock is associated with a business cycle. From the empirical analysis, both the factor and the idiosyncratic component have fat-tail error distributions, and the estimated CI and recession probabilities are close to those published by the Economic and Social Research Institute. Compared with the estimated CI using the adjusted data set, the outlier adjustment reduces the depth of the recession. Moreover, the results of the shock decomposition show that the financial crisis in mid-2008 was caused by increase of clustering shocks and large unexpected shocks. In contrast, the Great East Japan Earthquake in 2011 was derived from idiosyncratic noise and did not cause a recession. When analyzing whether to use a sample that includes outliers associated with the business cycle, it is not desirable to use the outlier-adjusted data set.
KeywordsBusiness cycle inference Heavy-tailed distribution Markov chain Monte Carlo (MCMC) Markov switching dynamic factor model Stochastic volatility
JEL ClassificationC11 C31 C32 R12
We gratefully acknowledge helpful the discussions and suggestions of Yasutomo Murasawa and Toshiaki Watanabe and especially, two anonymous referees regarding several points in the paper. This research is supported by a grant-in-aid from Zengin Foundation for Studies on Economics and Finance.
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