Bayesian estimation of DSGE models with Hamiltonian Monte Carlo

Mátyás Farkas and Balint Tatar

No 144, IMFS Working Paper Series from Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)

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.

Keywords: DSGE Estimation; Bayesian Analysis; Hamiltonian Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C15 E10 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:imfswp:144

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