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LARGE SAMPLE PROPERTIES OF BAYESIAN ESTIMATION OF SPATIAL ECONOMETRIC MODELS

Published online by Cambridge University Press:  11 August 2020

Xiaoyi Han ,
Lung-Fei Lee  and
Xingbai Xu
Xiaoyi Han
Affiliation:
The Wang Yanan Institute for Studies in Economics, Xiamen University
Lung-Fei Lee
Affiliation:
The Ohio State University
Xingbai Xu*
Affiliation:
Xiamen University
*
Address correspondence to Xingbai Xu, MOE Key Laboratory of Econometrics, Fujian Key Laboratory of Statistical Science, The Wang Yanan Institute for Studies in Economics, Department of Statistics, School of Economics, Xiamen University, Xiamen, China; e-mail: xu.510@buckeyemail.osu.edu.
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Abstract

This paper studies asymptotic properties of a posterior probability density and Bayesian estimators of spatial econometric models in the classical statistical framework. We focus on the high-order spatial autoregressive model with spatial autoregressive disturbance terms, due to a computational advantage of Bayesian estimation. We also study the asymptotic properties of Bayesian estimation of the spatial autoregressive Tobit model, as an example of nonlinear spatial models. Simulation studies show that even when the sample size is small or moderate, the posterior distribution of parameters is well approximated by a normal distribution, and Bayesian estimators have satisfactory performance, as classical large sample theory predicts.

Type
ARTICLES
Information
Econometric Theory , Volume 37 , Issue 4 , August 2021 , pp. 708 - 746
Copyright
© Cambridge University Press 2020

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Footnotes

We are grateful to the editor, the co-editor, and four anonymous referees for their helpful comments. X.H. gratefully acknowledges the financial support of the Chinese Natural Science Fund (Nos. 71501163 and 71973113), and the Fundamental Research Funds for the Central Universities (20720151144) to Xiamen University. X.X. gratefully acknowledges the financial support of the Fundamental Research Funds for the Central Universities (Nos. 20720171072 and 20720181052) to Xiamen University, and the Chinese Natural Science Fund (Nos. 71703135 and 11771361). We also thank the help from IT Engineer Xiang Li at the School of Economics, Xiamen University.

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