BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS
The fixed effects estimator of panel models can be severely biased because of well-known incidental parameter problems. It is shown that this bias can be reduced in nonlinear dynamic panel models. We consider asymptotics where n and T grow at the same rate as an approximation that facilitates compar...
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| Veröffentlicht in: | Econometric theory 2011-12, Vol.27 (6), p.1152-1191 |
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| container_title | Econometric theory |
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| creator | Hahn, Jinyong Kuersteiner, Guido |
| description | The fixed effects estimator of panel models can be severely biased because of well-known incidental parameter problems. It is shown that this bias can be reduced in nonlinear dynamic panel models. We consider asymptotics where n and T grow at the same rate as an approximation that facilitates comparison of bias properties. Under these asymptotics, the bias-corrected estimators we propose are centered at the truth, whereas fixed effects estimators are not. We discuss several examples and provide Monte Carlo evidence for the small sample performance of our procedure. |
| doi_str_mv | 10.1017/S0266466611000028 |
| format | Article |
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| language | eng |
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| source | Cambridge Journals; Jstor Complete Legacy |
| subjects | Approximation Bias Comparative analysis Data models Dynamic modeling Econometric models Econometrics Economic methodology Economic models Estimation Estimation bias Estimation methods Estimators Infinity Maximum likelihood estimation Normal distribution Studies Time series |
| title | BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS |
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