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Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters

by Wen Xu
Department of Economics, Linacre College, University of Oxford, St Cross Road, Oxford OX1 3JA, UK
Academic Editors: In Choi and Ryo Okui
Econometrics 2016, 4(4), 39; https://doi.org/10.3390/econometrics4040039
Received: 6 May 2016 / Revised: 18 September 2016 / Accepted: 26 September 2016 / Published: 9 October 2016
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
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. View Full-Text
Keywords: dynamic panel data models; stochastic volatility; particle filters; state space modeling dynamic panel data models; stochastic volatility; particle filters; state space modeling
MDPI and ACS Style

Xu, W. Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters. Econometrics 2016, 4, 39.

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