Publication Type

Journal Article

Version

Postprint

Publication Date

11-2015

Abstract

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.

Keywords

Asymptotic inference, Bias correction, Bootstrap, Improved t-ratio, Monte Carlo, Spatial layout, Stochastic expansion, Variance correction

Discipline

Econometrics

Research Areas

Econometrics

Publication

Regional Science and Urban Economics

Volume

55

First Page

55

Last Page

67

ISSN

0166-0462

Identifier

10.1016/j.regsciurbeco.2015.08.004

Publisher

Elsevier

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1016/j.regsciurbeco.2015.08.004

Included in

Econometrics Commons

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