Arbeitspapier
Estimation of DSGE models: Maximum likelihood vs. Bayesian methods
DSGE models are typically estimated using Bayesian methods, but a researcher may want to estimate a DSGE model with full information maximum likelihood (FIML) so as to avoid the use of prior distributions. A very robust algorithm is needed to find the global maximum within the relevant parameter space. I suggest such an algorithm and show that it is possible to estimate the model of Smets and Wouters (2007) using FIML. Inference is carried out using stochastic bootstrapping techniques. Several FIML estimates turn out to be significantly different from the Bayesian estimates and the reasons behind those differences are analyzed.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 2015:6
- Classification
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Wirtschaft
Bayesian Analysis: General
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Subject
-
Bayesian methods
Maximum likelihood
Business Cycles
Estimate DSGE models
- Event
-
Geistige Schöpfung
- (who)
-
Mickelsson, Glenn
- Event
-
Veröffentlichung
- (who)
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Uppsala University, Department of Economics
- (where)
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Uppsala
- (when)
-
2015
- Handle
- URN
-
urn:nbn:se:uu:diva-270200
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Mickelsson, Glenn
- Uppsala University, Department of Economics
Time of origin
- 2015
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