Artikel
Estimation of dynamic panel data models with stochastic volatility using particle filters
Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. Dynamic panel data models have become increasingly popular in macroeconomics to study common relationships across countries or regions. This paper estimates dynamic panel data models with stochastic volatility by maximizing an approximate likelihood obtained via Rao-Blackwellized particle filters. Monte Carlo studies reveal the good and stable performance of our particle filter-based estimator. When the volatility of volatility is high, or when regressors are absent but stochastic volatility exists, our approach can be better than the maximum likelihood estimator which neglects stochastic volatility and generalized method of moments (GMM) estimators.
- Language
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Englisch
- Bibliographic citation
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 4 ; Year: 2016 ; Issue: 4 ; Pages: 1-13 ; Basel: MDPI
- Classification
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Wirtschaft
Estimation: General
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Subject
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dynamic panel data models
stochastic volatility
particle filters
state space modeling
- Event
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Geistige Schöpfung
- (who)
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Xu, Wen
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
-
Basel
- (when)
-
2016
- DOI
-
doi:10.3390/econometrics4040039
- Handle
- Last update
-
10.03.2025, 11:43 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
- Artikel
Associated
- Xu, Wen
- MDPI
Time of origin
- 2016
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