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

Econometrics

卷:4

Wen Xu¹ 

[1] Department of Economics, Linacre College, University of Oxford, St Cross Road, Oxford OX1 3JA, UK;

关键词: dynamic panel data models;    stochastic volatility;    particle filters;    state space modeling;   

DOI  :  10.3390/econometrics4040039

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown