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Bayesian estimation of DSGE models

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  • Pablo A. Guerrón-Quintana
  • James M. Nason

Abstract

We survey Bayesian methods for estimating dynamic stochastic general equilibrium (DSGE) models in this article. We focus on New Keynesian (NK)DSGE models because of the interest shown in this class of models by economists in academic and policy-making institutions. This interest stems from the ability of this class of DSGE model to transmit real, nominal, and fiscal and monetary policy shocks into endogenous fluctuations at business cycle frequencies. Intuition about these propagation mechanisms is developed by reviewing the structure of a canonical NKDSGE model. Estimation and evaluation of the NKDSGE model rests on being able to detrend its optimality and equilibrium conditions, to construct a linear approximation of the model, to solve for its linear approximate decision rules, and to map from this solution into a state space model to generate Kalman filter projections. The likelihood of the linear approximate NKDSGE model is based on these projections. The projections and likelihood are useful inputs into the Metropolis-Hastings Markov chain Monte Carlo simulator that we employ to produce Bayesian estimates of the NKDSGE model. We discuss an algorithm that implements this simulator. This algorithm involves choosing priors of the NKDSGE model parameters and fixing initial conditions to start the simulator. The output of the simulator is posterior estimates of two NKDSGE models, which are summarized and compared to results in the existing literature. Given the posterior distributions, the NKDSGE models are evaluated with tools that determine which is most favored by the data. We also give a short history of DSGE model estimation as well as pointing to issues that are at the frontier of this research.

Suggested Citation

  • Pablo A. Guerrón-Quintana & James M. Nason, 2012. "Bayesian estimation of DSGE models," Working Papers 12-4, Federal Reserve Bank of Philadelphia.
  • Handle: RePEc:fip:fedpwp:12-4
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    File URL: http://www.philadelphiafed.org/research-and-data/publications/working-papers/2012/wp12-4.pdf
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    • Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512 Edward Elgar Publishing.

    References listed on IDEAS

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    1. repec:eee:dyncon:v:83:y:2017:i:c:p:215-231 is not listed on IDEAS
    2. Pawel Baranowski & Zbigniew Kuchta, 2015. "Changes in nominal rigidities in Poland – a regime switching DSGE perspective," Lodz Economics Working Papers 6/2015, University of Lodz, Faculty of Economics and Sociology.
    3. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    4. Shalva Mkhatrishvili & Zviad Zedginidze, 2015. "Modeling Macro-Fiscal Interlinkages: Case of Georgia," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 7(1), pages 15-41, March.
    5. Katayama, Munechika & Kim, Kwang Hwan, 2013. "The delayed effects of monetary shocks in a two-sector New Keynesian model," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 243-259.

    More about this item

    Keywords

    Bayesian statistical decision theory ; Markov processes ; Keynesian economics;

    JEL classification:

    • 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
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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