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Estimation of Spatial Regression Models with Autoregressive Errors by Two-Stage Least Squares Procedures: A Serious Problem

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  • Harry H. Kelejian

    (Department of Economics, University of Maryland, College Park, MD 20742 USA, kelejian@econ.umd.edu)

  • Ingmar R. Prucha

    (Department of Economics, University of Maryland, College Park, MD 20742 USA, prucha@econ.umd.edu)

Abstract

Time series regression models that have autoregressive errors are often estimated by two-stage procedures which are based on the Cochrane-Orcutt (1949) transformation. It seems natural to also attempt the estimation of spatial regression models whose error terms are autoregressive in terms of an analogous transformation. Various two-stage least squares procedures suggest themselves in this context, including an analog to Durbin's (1960) procedure. Indeed, these procedures are so suggestive and computationally convenient that they are quite "tempting." Unfortunately, however, as shown in this paper, these two-stage least squares procedures are generally, in a typical cross-sectional spatial context, not consistent and therefore should not be used.

Suggested Citation

  • Harry H. Kelejian & Ingmar R. Prucha, 1997. "Estimation of Spatial Regression Models with Autoregressive Errors by Two-Stage Least Squares Procedures: A Serious Problem," International Regional Science Review, , vol. 20(1-2), pages 103-111, April.
    • Handle: RePEc:sae:inrsre:v:20:y:1997:i:1-2:p:103-111
    DOI: 10.1177/016001769702000106
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    References listed on IDEAS

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    1. Dubin, Robin A, 1988. "Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms," The Review of Economics and Statistics, MIT Press, vol. 70(3), pages 466-474, August.
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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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