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Bayesian estimation of DSGE models with Hamiltonian Monte Carlo

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  • Farkas, Mátyás
  • Tatar, Balint

Abstract

In this paper we adopt the Hamiltonian Monte Carlo (HMC) estimator for DSGE models by implementing it into a state-of-the-art, freely available high-performance software package. We estimate a small scale textbook New-Keynesian model and the Smets-Wouters model on US data. Our results and sampling diagnostics con firm the parameter estimates available in existing literature. In addition we combine the HMC framework with the Sequential Monte Carlo (SMC) algorithm which permits the estimation of DSGE models with ill-behaved posterior densities.

Suggested Citation

  • Farkas, Mátyás & Tatar, Balint, 2020. "Bayesian estimation of DSGE models with Hamiltonian Monte Carlo," IMFS Working Paper Series 144, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    • Handle: RePEc:zbw:imfswp:144
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    Cited by:

    1. Böhl, Gregor, 2021. "Efficient solution and computation of models with occasionally binding constraints," IMFS Working Paper Series 148, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    2. Alfred Duncan, 2021. "Reverse mode differentiation for DSGE models," Studies in Economics 2108, School of Economics, University of Kent.

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    More about this item

    Keywords

    DSGE Estimation; Bayesian Analysis; Hamiltonian Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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