TY  - UNPD
A1  - Farkas, Mátyás
A1  - Tatar, Balint
T1  - Bayesian estimation of DSGE models with Hamiltonian Monte Carlo
T2  - Working paper series / Institute for Monetary and Financial Stability ; 144 [Version October 22, 2020]
N2  - 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-theart, 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.
T3  - Working paper series / Institute for Monetary and Financial Stability - 144 [v. 22 10.2020] 
KW  - DSGE Estimation
KW  - Bayesian Analysis
KW  - Hamiltonian Monte Carlo
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/55481
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-554818
UR  - https://www.imfs-frankfurt.de/fileadmin/user_upload/IMFS_WP/IMFS_WP_144.pdf
N1  - Eine ältere Version ist zu finden unter http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hebis:30:3-554705
PB  - Johann Wolfgang Goethe-Univ., Inst. for Monetary and Financial Stability
CY  - Frankfurt am Main
ER  -