Estimating a DSGE model for Japan in a data-rich environment
Ryoichi Namba and
Journal of the Japanese and International Economies, 2015, vol. 36, issue C, 25-55
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.
Keywords: DSGE; Business cycle; Data-rich estimation; Measurement error; MCMC; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C32 C53 E32 E37 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jjieco:v:36:y:2015:i:c:p:25-55
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