Estimating a DSGE model for Japan in a data-rich environment

Hirokuni Iiboshi, Tatsuyoshi Matsumae, Ryoichi Namba, Shin Ichi Nishiyama

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


A dynamic factor model (DFM), widely used in empirical research in macroeconomics, shows that common factors extracted from large panel data sets are key factors behind the fluctuations of primal macroeconomic series. Boivin and Giannoni (2006) and Kryshko (2011) combine a dynamic stochastic general equilibrium (DSGE) model with a DFM as a data-rich DSGE model, in which model variables are regarded as common factors derived from large data sets. Following Smets and Wouters (2003, 2007), we estimate a new Keynesian DSGE model for Japan between 1981Q1 and 1995Q4 in a data-rich environment with 55 macroeconomic indicators using Markov chain Monte Carlo (MCMC) methods. Using a simulation smoother developed by de Jong and Shephard (1995), unlike previous studies, we succeeded in sampling model variables and exogenous shocks used for analyzing sources of business cycles. We found that a data-rich DSGE model with an inappropriate data set or inaccurate specificities reduces efficiency even though the number of indicators is fulfilling.

Original languageEnglish
Pages (from-to)25-55
Number of pages31
JournalJournal of the Japanese and International Economies
Publication statusPublished - 2015 Jun 1


  • Bayesian estimation
  • Business cycle
  • DSGE
  • Data-rich estimation
  • MCMC
  • Measurement error

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics
  • Political Science and International Relations


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