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Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data

Published online by Cambridge University Press:  04 January 2017

Robert J. Franzese Jr  and
Jude C. Hays
Robert J. Franzese Jr
Affiliation:
Department of Political Science, University of Michigan, Ann Arbor, MI 48109. e-mail: [email protected] (corresponding author)
Jude C. Hays
Affiliation:
Department of Political Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL 61801. e-mail: [email protected]
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Abstract

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.

Type
Research Article
Information
Political Analysis , Volume 15 , Issue 2: Special Issue: Time-Series Cross-Sectional Analysis , Spring 2007 , pp. 140 - 164
Copyright
Copyright © The Author 2007. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Anselin, L. 1988. Spatial econometrics: Methods and models. Boston: Kluwer Academic.Google Scholar
Anselin, L. 2001. Spatial econometrics. In A companion to theoretical econometrics, ed. Baltagi, B., 310–30. Oxford: Basil Blackwell.Google Scholar
Anselin, L. 2003. Spatial externalities, spatial multipliers, and spatial econometrics. International Regional Science Review 26: 153–66.Google Scholar
Bartels, L. 1991. Instrumental and ‘quasi-instrumental’ variables. American Journal of Political Science 35: 777–800.Google Scholar
Beck, N., Gleditsch, K. S., and Beardsley, K. 2006. Space is more than geography: Using spatial econometrics in the study of political economy. International Studies Quarterly 50: 27–44.CrossRefGoogle Scholar
Beck, N., and Katz, J. 1995. What to do (and not to do) with time series cross section data in comparative politics. American Political Science Review 89: 634–47.CrossRefGoogle Scholar
Beck, N., and Katz, J. 1996. Nuisance vs. substance: Specifying and estimating time series cross section models. Political Analysis 6: 1–34.CrossRefGoogle Scholar
De Boef, S., and Keele, L. 2006. Taking time seriously, dynamic regression models. Paper presented at the annual meeting of the Political Methodology Society, Tallahasee, FL.Google Scholar
Elhorst, J. P. 2001. Dynamic models in space and time. Geographical Analysis 33: 119–140.CrossRefGoogle Scholar
Elhorst, J. P. 2003. Specification and estimation of spatial panel data models. International Regional Science Review 26: 244–68.Google Scholar
Elhorst, J. P. 2005. Unconditional maximum likelihood estimation of linear and log-linear dynamic models for spatial panels. Geographical Analysis 37: 85–106.Google Scholar
Franzese, R., and Hays, J. 2004. Empirical modeling strategies for spatial interdependence: Omitted-variable vs. simultaneity biases. Summer meetings of the Political Methodology Society.Google Scholar
Franzese, R., and Hays, J. 2006a. Spatio-temporal models for political-science panel and time-series-crosssection data. Summer meetings of the Political Methodology Society.Google Scholar
Franzese, R., and Hays, J. 2006b. Strategic interaction among EU governments in active-labor-market policymaking: Subsidiarity and policy coordination under the European employment strategy. European Union Politics 7: 167–89.CrossRefGoogle Scholar
Kam, C., and Franzese, R. 2007. Modeling and interpreting interactive hypotheses in regression analysis. Ann Arbor: University of Michigan Press.Google Scholar
Kelejian, H. H., Prucha, I. R., and Yuzeforich, Y. 2004. Instrumental variable estimation of a spatial autoregressive model with autoregressive disturbances: Large and small sample results. Advances in Econometrics 18: 163–198.Google Scholar
Kelejian, H. H., and Robinson, D. P. 1993. A suggested method of estimation for spatial interdependent models with autocorrelated errors and an application to a county expenditure model. Papers in Regional Science 72: 297–312.CrossRefGoogle Scholar
LeSage, J. P. 1999. Spatial econometrics. http://rri.wvu.edu/WebBook/LeSage/spatial/spatial.html.Google Scholar
Lin, T. M., Wu, C. E., and Lee, F. Y. 2006. ‘Neighborhood’ influence on the formation of national identity in Taiwan: Spatial regression with disjoint neighborhoods. Political Research Quarterly 59: 35–46.Google Scholar
Ord, K. 1975. Estimation methods for models of spatial interaction. Journal of the American Statistical Association 70: 120–6.Google Scholar