Simulation Study on Estimation Bias in Spatial Lag Model from Omitted Variable Correlated with Regressors

23 Pages Posted: 10 Nov 2012

See all articles by Takahisa Yokoi

Takahisa Yokoi

Tohoku University - Graduate School of Information Sciences

Date Written: November 9, 2012

Abstract

In this research, the omitted variable problem in a spatial autoregressive model is analyzed by simulation. We examine the performances of estimators when an omitted variable is correlated with explanatory variables. In the literature, theoretical aspects of estimating spatial autoregressive models have been discussed including the spatial error model for the spatially autocorrelated omitted variable. Regarding the ideal case of the spatial lag model, in which there is not an omitted variable correlated with regressors, there have been theoretical discussions of consistency and simulation analyses on the small sample property of the estimator. In the case of real data, some important variables may not be available and most socioeconomic variables are mutually interdependent. Consequently, the performance of estimation methods should be verified in such cases. In this research, we compared three estimation methods for the spatial lag model, namely, maximum likelihood (ML), spatial two-stage least squares (S-2SLS), and the general method of moments (GMM), by using two definitions of the root mean square error. Our simulation results show that the S-2SLS estimator is strongly affected by the omitted variable under certain conditions.

Keywords: Spatial autoregressive model, Spatial lag model, Omitted variable bias, Maximum likelihood estimation, Spatial two-stage least squares, General method of moments

JEL Classification: C13, C21, C51

Suggested Citation

Yokoi, Takahisa, Simulation Study on Estimation Bias in Spatial Lag Model from Omitted Variable Correlated with Regressors (November 9, 2012). Available at SSRN: https://ssrn.com/abstract=2173125 or http://dx.doi.org/10.2139/ssrn.2173125

Takahisa Yokoi (Contact Author)

Tohoku University - Graduate School of Information Sciences ( email )

Aoba 6-3-09
Aramaki, Aoba-ku
Sendai, Miyagi 980-8579
Japan

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