Bayesian Estimation of DSGE Models with Hamiltonian Monte Carlo (60min)

Category: Money and Macro Brown Bag Seminar
When: 09 Dezember 2021
, 13:30
 - 14:30
Where: online
Speaker: Balint Tatar

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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