Bias correction for fixed effects spatial panel data models

Bias correction for fixed effects spatial panel data models

This paper examines the finite sample properties of the quasi maximum likelihood (QML) estimators of the fixed effects spatial panel data (FE-SPD) models of Lee and Yu (2010). Following the general bias correction methods recently developed by Yang (2015), we derive up to third-order bias correction...

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Bibliographic Details
Main Authors: Yang, Zhenlin, YU, Jihai, LIU, Shew Fan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
Bias correction
Variance correction
Bootstrap
Spatial panel
Individual fixed effects
Time fixed effects
Quasi maximum likelihood
Spatial lag
Spatial error
Spatial ARAR
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/1754
https://ink.library.smu.edu.sg/context/soe_research/article/2753/viewcontent/04_2015.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:This paper examines the finite sample properties of the quasi maximum likelihood (QML) estimators of the fixed effects spatial panel data (FE-SPD) models of Lee and Yu (2010). Following the general bias correction methods recently developed by Yang (2015), we derive up to third-order bias corrections for the QML estimators of the FE-SPD model, and propose a simple bootstrap method for their practical implementation. Monte Carlo results reveal that the QML estimators of the spatial parameters can be quite biased and that a second-order bias correction effectively removes the bias. The validity of the bootstrap method is established. Variance corrections are also considered, which together with bias corrections lead to improved inferences.

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