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Bias correction and refined inferences for fixed effects spatial panel data models
This paper first presents simple methods for conducting up to third-order bias and variance corrections for the quasi maximum likelihood (QML) estimators of the spatial parameter(s) in the fixed effects spatial panel data (FE-SPD) models. Then, it shows how the bias and variance corrections lead to refined t-ratios for spatial effects and for covariate effects. The implementation of these corrections depends on the proposed bootstrap methods of which validity is established. Monte Carlo results reveal that (i) the QML estimators of the spatial parameters can be quite biased, (ii) a second-order bias correction effectively removes the bias, and (iii) the proposed t-ratios are much more reliable than the usual t-ratios. [web URL: http://www.sciencedirect.com/science/article/pii/S0166046216301569]
Bias correction and refined inferences for fixed effects spatial panel data models
This paper first presents simple methods for conducting up to third-order bias and variance corrections for the quasi maximum likelihood (QML) estimators of the spatial parameter(s) in the fixed effects spatial panel data (FE-SPD) models. Then, it shows how the bias and variance corrections lead to refined t-ratios for spatial effects and for covariate effects. The implementation of these corrections depends on the proposed bootstrap methods of which validity is established. Monte Carlo results reveal that (i) the QML estimators of the spatial parameters can be quite biased, (ii) a second-order bias correction effectively removes the bias, and (iii) the proposed t-ratios are much more reliable than the usual t-ratios. [web URL: http://www.sciencedirect.com/science/article/pii/S0166046216301569]
Bias correction and refined inferences for fixed effects spatial panel data models
Yang, Zhenlin (author) / Yu, Jihai / Liu, Shew Fan
2016
Article (Journal)
English
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