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Bias from Network Misspecification Under Spatial Dependence

Published online by Cambridge University Press:  23 November 2020

Timm Betz ,
Scott J. Cook Open the ORCID record for Scott J. Cook [Opens in a new window]  and
Florian M. Hollenbach
Timm Betz
Affiliation:
Bavarian School of Public Policy, TUM School of Governance, Technical University of Munich, Munich, Germany. Email: timm.betz@tum.de, URL: timm-betz.de
Scott J. Cook*
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA. Email: sjcook@tamu.edu, URL: http://www.scottjcook.net
Florian M. Hollenbach
Affiliation:
Department of Political Science, Texas A&M University, College Station, TX77843, USA. Email: fhollenbach@tamu.edu, URL: http://www.fhollenbach.org
*
Corresponding author Scott J. Cook
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Abstract

The prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bounds for the bias in spatial econometric models with nondifferential error in the specification of the weights matrix. Under these conditions, we demonstrate that an omitted spatial input is the limit condition of including a misspecificed spatial weights matrix. Simulated experiments further demonstrate that spatial models with a misspecified weights matrix weakly dominate nonspatial models. Our results imply that, where cross-sectional dependence is presumed, researchers should pursue spatial analysis even with limited information on network ties.

Keywords

spatial dependenceboundsomitted variablesmeasurement error
Type
Letter
Information
Political Analysis , Volume 29 , Issue 2 , April 2021 , pp. 260 - 266
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

References

Anselin, L. 1988. Spatial Econometrics: Methods and Models. Dordrecht, the Netherlands: Springer Science & Business Media.Google Scholar
Arel-Bundock, V., Blais, A., and Dassonneville, R.. 2019. “Do Voters Benchmark Economic Performance?” British Journal of Political Science, 1–13. doi:10.1017/S0007123418000236.Google Scholar
Betz, T., Cook, S. J., and Hollenbach, F. M.. 2020a. “Spatial Interdependence and Instrumental Variable Models.” Political Science Research and Methods 8(4):646–661.Google Scholar
Betz, T., Cook, S. J., and Hollenbach, F. M.. 2020b. “Bias Due to Network Misspecification Under Spatial Dependence.” Code Ocean, V1. https://codeocean.com/capsule/7618094/tree.Google Scholar
Betz, T., Cook, S. J., and Hollenbach, F. M.. 2020c. “Replication Data for: Bias Due to Network Misspecification Under Spatial Dependence.” https://doi.org/10.7910/DVN/ADIFOV, Harvard Dataverse, V1, UNF:6:EubeWsg/18JKC5tPaIAePw== [fileUNF].Google Scholar
Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M. 2006. Measurement Error in Nonlinear Models: A Modern Perspective. Boca Raton, FL: Chapman and Hall/CRC Press.CrossRefGoogle Scholar
Cliff, A. D., and Ord, J. K.. 1981. Spatial Processes: Models and Applications. London: Pion Ltd.Google Scholar
Corrado, L., and Fingleton, B.. 2012. “Where is the Economics in Spatial Econometrics?” Journal of Regional Science 52(2):210–239.CrossRefGoogle Scholar
Franzese, R. J. Jr., and Hays, J. C.. 2007. “Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15(2):140–164.Google Scholar
Griffith, D. A. 1996. “Some Guidelines for Specifying the Geographic Weights Matrix Contained in Spatial Statistical Models.” In Practical Handbook of Spatial Statistics, edited by Arlinghaus, S. L., 65–82. Boca Raton, FL: CRC Press.Google Scholar
Halleck Vega, S., and Elhorst, J. P.. 2015. “The SLX Model.” Journal of Regional Science 55(3):339–363.Google Scholar
Juhl, S. 2020. “The Sensitivity of Spatial Regression Models to Network Misspecification.” Political Analysis 28(1):1–19.Google Scholar
Kayser, M. A., and Peress, M.. 2012. “Benchmarking across Borders: Electoral Accountability and the Necessity of Comparison.” American Political Science Review 106(3):661–684.Google Scholar
LeSage, J., and Pace, R.. 2014. “The Biggest Myth in Spatial Econometrics.” Econometrics 2(4):217–249.CrossRefGoogle Scholar
LeSage, J., and Pace, R. K.. 2009. Introduction to Spatial Econometrics. Boca Raton, FL: Chapman and Hall/CRC Press.Google Scholar
Neumayer, E., and Plümper, T.. 2016. “W.” Political Science Research and Methods 4(1):175–193.CrossRefGoogle Scholar
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https://doi.org/10.7910/DVN/ADIFOV
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Betz et al. Supplementary Materials

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