Using Bayesian Posterior Model Probabilities to Identify Omitted Variables in Spatial Regression Models

37 Pages Posted: 13 Nov 2012

See all articles by Donald J. Lacombe

Donald J. Lacombe

West Virginia University - Regional Research Institute

James P. LeSage

Texas State University - McCoy College of Business Administration

Date Written: November 12, 2012

Abstract

LeSage and Pace (2009) consider the impact of omitted variables in the face of spatial dependence in the disturbance process of a linear regression relationship. Remarkably, they show that this can lead to a spatial regression model specification containing a spatial lag of the dependent and explanatory variables, providing an econometric as opposed to economic theory motivation for use of spatial regression models in applied work. Another implication of this econometric situation is the presence of global spatial spillovers. Global spillovers reflect a situation where changes in a characteristic of one observation/region (potentially) impacts outcomes in all other observations/regions. Specifically, global spillovers impact the neighbors, neighbors to the neighbors, neighbors to the neighbors to the neighbors, and so on, with decreasing influence as we move to higher-order/more distant neighbors.

We explore the econometric role played by omitted variables under alternative scenarios that give rise to both spatial regression specifications involving either local or global spatial spillovers. Local spillovers are those that impact only immediately neighboring observations, but not higher-order neighbors (e.g., neighbors to neighbors and so on). In addition to theoretical consideration of the alternative scenarios that practitioners might use to identify an appropriate local versus global spatial spillover specification, we explore performance of Bayesian model comparison methods in distinguishing between local and global spillover specifications that arise with varying levels of correlation between included an omitted variables.

Keywords: local vs. global spatial spillovers, Bayesian model comparison methods

JEL Classification: C11, C31

Suggested Citation

Lacombe, Donald J. and LeSage, James P., Using Bayesian Posterior Model Probabilities to Identify Omitted Variables in Spatial Regression Models (November 12, 2012). Available at SSRN: https://ssrn.com/abstract=2174610 or http://dx.doi.org/10.2139/ssrn.2174610

Donald J. Lacombe (Contact Author)

West Virginia University - Regional Research Institute ( email )

P.O. Box 6825
Morgantown, WV 26506-6825
United States
1-304-293-8543 (Phone)
1-304-293-6699 (Fax)

HOME PAGE: http://community.wvu.edu/~djl041/

James P. LeSage

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

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
130
Abstract Views
759
rank
261,937
PlumX Metrics