Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/171898 
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Erscheinungsjahr: 
2016
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2016 [Pages:] 1-13
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
dynamic panel data models
stochastic volatility
particle filters
state space modeling
JEL: 
C13
C15
C23
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