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A Further Exploration Into The Robustness Of Spatial Autocorrelation Specifications

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  • Takafumi Kato

Abstract

ABSTRACT In a recent study, the robustness of linear models with various spatial autocorrelation specifications was assessed through Monte Carlo experiments, and the geostatistical models were concluded to dominate the weight matrix models in the prediction. The present study tests the soundness of this conclusion with a different framework for prediction and presents some experimental results that can call into doubt the dominance of the geostatistical models over the weight matrix models.

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  • Takafumi Kato, 2008. "A Further Exploration Into The Robustness Of Spatial Autocorrelation Specifications," Journal of Regional Science, Wiley Blackwell, vol. 48(3), pages 615-639, August.
    • Handle: RePEc:bla:jregsc:v:48:y:2008:i:3:p:615-639
    DOI: 10.1111/j.1467-9787.2008.00566_1.x
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    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2005. "Spatial Dependence, Housing Submarkets, and House Prices," FAME Research Paper Series rp151, International Center for Financial Asset Management and Engineering.
    3. Robin Dubin, 2003. "Robustness of Spatial Autocorrelation Specifications: Some Monte Carlo Evidence," Journal of Regional Science, Wiley Blackwell, vol. 43(2), pages 221-248, May.
    4. R. Kelley Pace, 1998. "Total Grid Estimation," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 101-114.
    5. Luc Anselin, 2001. "Spatial Effects in Econometric Practice in Environmental and Resource Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 705-710.
    6. Pace, R Kelley & Gilley, Otis W, 1997. "Using the Spatial Configuration of the Data to Improve Estimation," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 333-340, May.
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    Cited by:

    1. Takafumi Kato, 2020. "Likelihood-based strategies for estimating unknown parameters and predicting missing data in the simultaneous autoregressive model," Journal of Geographical Systems, Springer, vol. 22(1), pages 143-176, January.
    2. Kato, Takafumi, 2013. "A comparison of spatial error models through Monte Carlo experiments," Economic Modelling, Elsevier, vol. 30(C), pages 743-753.
    3. Takafumi Kato, 2013. "Usefulness of the Information Contained in the Prediction Sample for the Spatial Error Model," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 169-195, July.
    4. Kato, Takafumi, 2012. "Prediction in the lognormal regression model with spatial error dependence," Journal of Housing Economics, Elsevier, vol. 21(1), pages 66-76.

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