Political Analysis Advance Access published online on March 16, 2007
Political Analysis, doi:10.1093/pan/mpm005
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Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data
Department of Political Science, University of Michigan, Ann Arbor, MI 48109
Department of Political Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL 61801, e-mail: jchays{at}uiuc.edu
e-mail: franzese{at}umich.edu (corresponding author)
In this paper, we demonstrate the econometric consequences of different specification and estimation choices in the analysis of spatially interdependent data and show how to calculate and present spatial effect estimates substantively. We consider four common estimators—nonspatial OLS, spatial OLS, spatial 2SLS, and spatial ML. We examine analytically the respective omitted-variable and simultaneity biases of nonspatial OLS and spatial OLS in the simplest case and then evaluate the performance of all four estimators in bias, efficiency, and SE accuracy terms under more realistic conditions using Monte Carlo experiments. We provide empirical illustration, showing how to calculate and present spatial effect estimates effectively, using data on European governments' active labor market expenditures. Our main conclusions are that spatial OLS, despite its simultaneity, performs acceptably under low-to-moderate interdependence strength and reasonable sample dimensions. Spatial 2SLS or spatial ML may be advised for other conditions, but, unless interdependence is truly absent or minuscule, any of the spatial estimators unambiguously, and often dramatically, dominates on all three criteria the nonspatial OLS commonly used currently in empirical work in political science.
Authors' note: This research was supported in part by National Science Foundation grant no. 0318045. We thank Chris Achen, Jim Alt, Kenichi Ariga, Neal Beck, Jake Bowers, Kerwin Charles, Bryce Corrigan, Tom Cusack, David Darmofal, Jakob de Haan, John Dinardo, Zach Elkins, John Freeman, Fabrizio Gilardi, Kristian Gleditsch, Mark Hallerberg, John Jackson, Aya Kachi, Jonathan Katz, Mark Kayser, Achim Kemmerling, Gary King, Hasan Kirmanoglu, James Kuklinski, Tse-Min Lin, Xiaobo Lu, Walter Mebane, Covadonga Meseguer, Michael Peress, Thomas Pluemper, Dennis Quinn, Megan Reif, Frances Rosenbluth, Ken Scheve, Phil Schrodt, Beth Simmons, Duane Swank, Wendy Tam Cho, Craig Volden, Michael Ward, and Gregory J. Wawro for useful comments on this and/or other work in our broader project on spatial econometric models in political science. Bryce Corrigan, Aya Kachi, and Xiaobo Lu each provided excellent research assistance and Kristian Gleditsch, Mark Hallerberg, and Duane Swank also generously shared data. We alone are responsible for any errors.
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