Biases of OLS and Spatial Lag Models in the Presence of an Omitted Variable and Spatially Dependent Variables

13 Pages Posted: 15 May 2008

See all articles by R. Kelley Pace

R. Kelley Pace

Louisiana State University - E.J. Ourso College of Business Administration

James P. LeSage

Texas State University - McCoy College of Business Administration

Date Written: Februrary 19, 2008

Abstract

A number of authors have suggested that omitted variables affect spatial regression methods less than ordinary least-squares (OLS). To explore these conjectures, we derive an expression for OLS omitted variable bias in a univariate model with spatial dependence and show that positive dependence in the disturbances, regressand, and regressor magnifies the magnitude of conventional omitted variables bias. Moreover, we show that spatial dependence in the regressor exacerbates the usual bias that occurs when using OLS to estimate a spatial autoregressive data generating process (DGP).

Keywords: Spatial dependence bias, least-squares regression

JEL Classification: C11, C15, R12, R11

Suggested Citation

Pace, R. Kelley and LeSage, James P., Biases of OLS and Spatial Lag Models in the Presence of an Omitted Variable and Spatially Dependent Variables (Februrary 19, 2008). Available at SSRN: https://ssrn.com/abstract=1133438 or http://dx.doi.org/10.2139/ssrn.1133438

R. Kelley Pace

Louisiana State University - E.J. Ourso College of Business Administration ( email )

Department of Finance
2164 B Patrick F. Taylor Hall
Baton Rouge, LA 70803-6308
United States
(225)-578-6256 (Phone)
(225)-578-9065 (Fax)

HOME PAGE: http://www.spatial-statistics.com

James P. LeSage (Contact Author)

Texas State University - McCoy College of Business Administration ( email )

Finanace and Economics Department
601 University Drive
San Marcos, TX 78666
United States
512-245-0256 (Phone)
512-245-3089 (Fax)

HOME PAGE: http://www.spatial-econometrics.com

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