Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models
License
Access Rights
Cadmus Permanent Link
Full-text via DOI
ISBN
ISSN
1872-7352; 0167-9473
Issue Date
Type of Publication
Keyword(s)
LC Subject Heading
Other Topic(s)
EUI Research Cluster(s)
Initial version
Published version
Succeeding version
Preceding version
Published version part
Earlier different version
Initial format
Citation
Computational Statistics and Data Analysis, 2014, Vol. 71, pp. 298-323
Cite
BEKIROS, Stelios D., PACCAGNINI, Alessia, Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models, Computational Statistics and Data Analysis, 2014, Vol. 71, pp. 298-323 - https://hdl.handle.net/1814/29544
Abstract
Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. A comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models is performed, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE–VAR and Factor Augmented DSGEs and tested against standard, Bayesian and Factor Augmented VARs. Moreover, small scale models including the real gross domestic product, the harmonized consumer price index and the nominal short-term federal funds interest rate, are comparatively assessed against medium scale models featuring additionally sticky nominal prices, wage contracts, habit formation, variable capital utilization and investment adjustment costs. The investigated period spans 1960:Q4–2010:Q4 and forecasts are produced for the out-of-sample testing period 1997:Q1–2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.