Universitätspublikationen
Refine
Year of publication
- 2020 (2)
Document Type
- Working Paper (2) (remove)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- Bayesian Analysis (2)
- DSGE Estimation (2)
- Hamiltonian Monte Carlo (2)
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.
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 confirm 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.