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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Econometrics |
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| Online Access: | http://www.mdpi.com/2225-1146/4/4/39 |
| _version_ | 1811306181387354112 |
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| author | Wen Xu |
| author_facet | Wen Xu |
| author_sort | Wen Xu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:39:46Z |
| format | Article |
| id | doaj.art-3cf64277447545d58f8d43570bd529a1 |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-04-13T08:39:46Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-3cf64277447545d58f8d43570bd529a12022-12-22T02:53:56ZengMDPI AGEconometrics2225-11462016-10-01443910.3390/econometrics4040039econometrics4040039Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle FiltersWen Xu0Department of Economics, Linacre College, University of Oxford, St Cross Road, Oxford OX1 3JA, UKTime-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.http://www.mdpi.com/2225-1146/4/4/39dynamic panel data modelsstochastic volatilityparticle filtersstate space modeling |
| spellingShingle | Wen Xu Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters Econometrics dynamic panel data models stochastic volatility particle filters state space modeling |
| title | Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters |
| title_full | Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters |
| title_fullStr | Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters |
| title_full_unstemmed | Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters |
| title_short | Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters |
| title_sort | estimation of dynamic panel data models with stochastic volatility using particle filters |
| topic | dynamic panel data models stochastic volatility particle filters state space modeling |
| url | http://www.mdpi.com/2225-1146/4/4/39 |
| work_keys_str_mv | AT wenxu estimationofdynamicpaneldatamodelswithstochasticvolatilityusingparticlefilters |