Bayesian Estimation of DSGE Models with Hamiltonian Monte Carlo

Forschungsbereich: Macroeconomics
Forscher: Mátyás Farkas,
Balint Tatar
Datum: Aug 2020
Abstract:

In this paper we adapt the Hamiltonian Monte Carlo (HMC) estimator to

DSGE models, a method presently used in various fields due to its

superior sampling and diagnostic properties. We implement it into a

state-of-the-art, freely available high-performance software package,

STAN. We estimate a small scale textbook New-Keynesian model and the

Smets-Wouters model using US data. Our results and sampling diagnostics

confirm the parameter estimates available in existing literature. In

addition, we find bimodality in the Smets-Wouters model even if we

estimate the model using the original tight priors. Finally, we combine

the HMC framework with the Sequential Monte Carlo (SMC) algorithm to

create a powerful tool which permits the estimation of DSGE models with

ill-behaved posterior densities.

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