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Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote

Author

Listed:
  • Donald J. Lacombe

    (Ohio University)

  • Timothy M. Shaughnessy

    (LSU in Shreveport)

Abstract

One problem with describing election vote shares using ordinary least squares (OLS) is that it ignores the possible presence of spatial error correlation, whereby the errors are correlated in a systematic manner over space. This omission can bias OLS standard errors. We examine the 2004 presidential county vote outcome using OLS and a spatial error model (SEM) that accounts for spatial autocorrelation in the error structure. We find that spatial error correlation is present, that the SEM is superior to OLS for making inferences, and that several factors deemed important to the 2004 election outcome are not significant once the spatial error autocorrelation is taken into account.

Suggested Citation

  • Donald J. Lacombe & Timothy M. Shaughnessy, 2007. "Accounting for Spatial Error Correlation in the 2004 Presidential Popular Vote," Public Finance Review, , vol. 35(4), pages 480-499, July.
    • Handle: RePEc:sae:pubfin:v:35:y:2007:i:4:p:480-499
    DOI: 10.1177/1091142106295768
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    References listed on IDEAS

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    Cited by:

    1. Burnett, Wesley & Lacombe, Donald J., 2012. "Accounting for Spatial Autocorrelation in the 2004 Presidential Popular Vote: A Reassessment of the Evidence," The Review of Regional Studies, Southern Regional Science Association, vol. 42(1), pages 75-89, Spring.
    2. Douglas M. Walker & Todd M. Nesbit, 2014. "Casino Revenue Sensitivity to Competing Casinos: A Spatial Analysis of Missouri," Growth and Change, Wiley Blackwell, vol. 45(1), pages 21-40, March.
    3. Robert Lawson & Todd Nesbit, 2013. "Alchian and Allen Revisited: Law Enforcement and the Price of Weed," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 41(4), pages 363-370, December.
    4. Le, Canh Quang & Li, Dong, 2008. "Double-Length Regression tests for testing functional forms and spatial error dependence," Economics Letters, Elsevier, vol. 101(3), pages 253-257, December.
    5. Donald J. Lacombe & James P. LeSage, 2018. "Use and interpretation of spatial autoregressive probit models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 1-24, January.

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