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Fast spatial estimation

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  • R. Kelley Pace
  • Ronald Barry

Abstract

Spatial estimators usually provide lower prediction errors than their aspatial counterparts. However, most of the standard techniques require a large number of operations. Fortunately, for a given observation only a relatively small number of nearby observations typically exhibit correlated errors. This means that most of the elements of the n by n spatial matrices are zero. The use of sparse matrix techniques can dramatically lower storage requirements and reduce execution times. In addition, adopting a first differencing model allows the use of GLS which avoids the necessity of evaluating an n by n determinant. This also greatly reduces computational costs.

Suggested Citation

  • R. Kelley Pace & Ronald Barry, 1997. "Fast spatial estimation," Applied Economics Letters, Taylor & Francis Journals, vol. 4(5), pages 337-341.
    • Handle: RePEc:taf:apeclt:v:4:y:1997:i:5:p:337-341
    DOI: 10.1080/758532605
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    Cited by:

    1. David M. Brasington & Diane Hite, 2005. "Demand for Environmental Quality: A Spatial Hedonic Approach," Departmental Working Papers 2005-08, Department of Economics, Louisiana State University.
    2. Brasington, David M. & Hite, Diane, 2005. "Demand for environmental quality: a spatial hedonic analysis," Regional Science and Urban Economics, Elsevier, vol. 35(1), pages 57-82, January.
    3. David M. Brasington & Diane Hite & Andres Jauregui, 2015. "House Price Impacts Of Racial, Income, Education, And Age Neighborhood Segregation," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 442-467, June.

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