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

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

Abstract

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.

Suggested Citation

  • Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Namba, Ryoichi & Nishiyama, Shin-Ichi, 2015. "Estimating a DSGE model for Japan in a data-rich environment," Journal of the Japanese and International Economies, Elsevier, vol. 36(C), pages 25-55.
    • Handle: RePEc:eee:jjieco:v:36:y:2015:i:c:p:25-55
    DOI: 10.1016/j.jjie.2015.02.001
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    2. MATSUMAE Tatsuyoshi & HASUMI Ryo, 2016. "Impacts of Government Spending on Unemployment: Evidence from a Medium-scale DSGE Model(in Japanese)," ESRI Discussion paper series 329, Economic and Social Research Institute (ESRI).
    3. IIBOSHI Hirokuni & MATSUMAE Tatsuyoshi & NISHIYAMA Shin-Ichi, 2014. "Sources of the Great Recession：A Bayesian Approach of a Data-Rich DSGE model with Time-Varying Volatility Shocks," ESRI Discussion paper series 313, Economic and Social Research Institute (ESRI).
    4. Hasumi, Ryo & Iiboshi, Hirokuni & Nakamura, Daisuke, 2018. "Trends, cycles and lost decades: Decomposition from a DSGE model with endogenous growth," Japan and the World Economy, Elsevier, vol. 46(C), pages 9-28.
    5. IIBOSHI Hirokuni, 2012. "Measuring the Effects of Monetary Policy: A DSGE-DFM Approach," ESRI Discussion paper series 292, Economic and Social Research Institute (ESRI).

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    More about this item

    Keywords

    DSGE; Business cycle; Data-rich estimation; Measurement error; MCMC; Bayesian estimation;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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