Improved Inferences for Spatial Regression Models

Improved Inferences for Spatial Regression Models

The quasi-maximum likelihood (QML) method is popular in the estimation and inference for spatial regression models. However, the QML estimators (QMLEs) of the spatial parameters can be quite biased and hence the standard inferences for the regression coefficients (based on t-ratios) can be seriously...

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Bibliographic Details
Main Authors: LIU, Shew Fan, YANG, Zhenlin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
Asymptotic inference
Bias correction
Bootstrap
Improved t-ratio
Monte Carlo
Spatial layout
Stochastic expansion
Variance correction
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/1786
https://ink.library.smu.edu.sg/context/soe_research/article/2785/viewcontent/LiuYangRSUE2015b.pdf
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Institution: Singapore Management University
Language: English
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Summary:The quasi-maximum likelihood (QML) method is popular in the estimation and inference for spatial regression models. However, the QML estimators (QMLEs) of the spatial parameters can be quite biased and hence the standard inferences for the regression coefficients (based on t-ratios) can be seriously affected. This issue, however, has not been addressed. The QMLEs of the spatial parameters can be bias-corrected based on the general method of Yang (2015b, J. of Econometrics 186, 178-200). In this paper, we demonstrate that by simply replacing the QMLEs of the spatial parameters by their bias-corrected versions, the usual t-ratios for the regression coefficients can be greatly improved. We propose further corrections on the standard errors of the QMLEs of the regression coefficients, and the resulted t-ratios perform superbly, leading to much more reliable inferences.

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