Indirect inference for dynamic panel models |
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Authors: | Christian Gouriéroux Peter CB Phillips Jun Yu |
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Institution: | 1. CREST-INSEE, 92245 Malakoff, France;2. Department of Economics, University of Toronto, Canada;3. Yale University, United States;4. University of Auckland, New Zealand;5. University of York, United Kingdom;6. Singapore Management University, Singapore;g School of Economics and Sim Kee Boon Institute for Financial Economics, Singapore Management University, 90 Stamford Road, Singapore 178903, Singapore |
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Abstract: | Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild increases in variance, thereby substantially reducing root mean square errors. The method is compared with certain consistent estimators and is shown to have superior finite sample properties to the generalized method of moment (GMM) and the bias-corrected ML estimator. |
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Keywords: | C33 |
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