Arbeitspapier
Bayesian estimation of DSGE models with Hamiltonian Monte Carlo
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: IMFS Working Paper Series ; No. 144
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
General Aggregative Models: General
- Thema
-
DSGE Estimation
Bayesian Analysis
Hamiltonian Monte Carlo
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Farkas, Mátyás
Tatar, Balint
- Ereignis
-
Veröffentlichung
- (wer)
-
Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2020
- Handle
- URN
-
urn:nbn:de:hebis:30:3-554705
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Farkas, Mátyás
- Tatar, Balint
- Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS)
Entstanden
- 2020
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