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Measurement Error and the Specification of the Weights Matrix in Spatial Regression Models

  • Garrett N. Vande Kamp (a1)

Abstract

While the spatial weights matrix $\boldsymbol{W}$ is at the core of spatial regression models, there is a scarcity of techniques for validating a given specification of $\boldsymbol{W}$ . I approach this problem from a measurement error perspective. When $\boldsymbol{W}$ is inflated by a constant, a predictable form of endogeneity occurs that is not problematic in other regression contexts. I use this insight to construct a theoretically appealing test and control for the validity of $\boldsymbol{W}$ that is tractable in panel data, which I call the K test. I demonstrate the utility of the test using Monte Carlo simulations.

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Copyright

Corresponding author

Footnotes

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Author’s note: Texas A&M University; garrettvandekamp@tamu.edu. The author would like to thank Alejandro Medina, Alison Higgins Merrill, Scott Cook, David Fortunato, Joseph Ura, Chris Schwarz, Desmond Wallace, John Poe, Robert Franzese, Paul Kellstedt, Yeshua de Nazarene, and two anonymous reviewers. In addition, I would like to thank the audiences of TexMeth 2018, Minnesota Political Methodology Graduate Student Conference, PolMeth 2018, and the Texas A&M Research Methods Workshop. Replication materials for this article are available from the Political Analysis Dataverse, cited at the end of this article.

Contributing Editor: Jeff Gill

Footnotes

References

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Bhattacharjee, A., and Jensen-Butler, C.. 2013. “Estimation of the Spatial Weights Matrix Under Structural Constraints.” Regional Science and Urban Economics 43(4):617634.
Buonaccorsi, J. P. 2010. Measurement Error: Models, Methods, and Applications . Chapman and Hall.
Corrado, L., and Fingleton, B.. 2012. “Where is the Economics in Spatial Econometrics? Journal of Regional Science 52(2):210239.
Franzese, R., and Hays, J.. 2007. “Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15:140164.
Greene, W. H. 2017. Econometric Analysis , 8th edn. Pearson.
Hays, J. C., Kachi, A., and Franzese, R.. 2010. “A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application.” Statistical Methodology 7(3):406428.
Harris, R., Moffat, J., and Kravtsova, V.. 2011. “In Search of ‘W’.” Spatial Economic Analysis 6(3):249270.
Kelejian, H. H., and Prucha, I. R.. 2002. “2SLS and OLS in a Spatial Autoregressive Model with Equal Spatial Weights.” Regional Science and Urban Economics 32(6):691707.
Kelejian, H. H., Prucha, I. R., and Yuzefovich, Y.. 2006. “Estimation Problems in Models with Spatial Weighting Matrices Which Have Blocks of Equal Elements.” Journal of Regional Science 46(3):507515.
Leenders, R. T. A. 2002. “Modeling Social Influence Through Network Autocorrelation: Constructing the Weight Matrix.” Social Networks 24(1):2147.
LeSage, J. P., and Pace, R. K.. 2014. “The Biggest Myth in Spatial Econometrics.” Econometrics 2(4):217249.
Gibbons, S., and Overman, H. G.. 2012. “Mostly Pointless Spatial Econometrics? Journal of Regional Science 52(2):172191.
Neumayer, E., and Plumper, T.. 2016. “W.” Political Science Research and Methods 4(1):175193.
Stakhovych, S., and Bijmolt, T.. 2009. “Specification of Spatial Models: A Simulation Study on Weights Matrices.” Papers in Regional Science 88(2):389408.
Vande Kamp, G. N.2019. “Replication Data for: Measurement Error and the Specification of the Weights Matrix in Spatial Regression Models,” https://doi.org/10.7910/DVN/W9DGRR, Harvard Dataverse, V1.
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Measurement Error and the Specification of the Weights Matrix in Spatial Regression Models

  • Garrett N. Vande Kamp (a1)

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