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A comparison of spatial error models through Monte Carlo experiments

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  • Kato, Takafumi

Abstract

A spatial error model is classified as a geostatistical model or a weight matrix model on the basis of the method of specification of spatial autocorrelation in the disturbance. Specification errors cannot be assumed to be absent, and the robustness of alternative specifications is useful for dealing with potential errors. Previous studies compared several models to arrive at two basic conclusions: (i) all of the models maintain reasonable estimation accuracy, and (ii) the two types of models have well-matched predictive abilities. The present study makes a supplementary comparison to investigate whether these conclusions are true for a broader range of models. Also, implications of our results for the model choice are explored.

Suggested Citation

  • Kato, Takafumi, 2013. "A comparison of spatial error models through Monte Carlo experiments," Economic Modelling, Elsevier, vol. 30(C), pages 743-753.
    • Handle: RePEc:eee:ecmode:v:30:y:2013:i:c:p:743-753
    DOI: 10.1016/j.econmod.2012.10.010
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    References listed on IDEAS

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    1. Takafumi Kato, 2008. "Response To Comment On “A Further Exploration Into The Robustness Of Spatial Autocorrelation Specifications”," Journal of Regional Science, Wiley Blackwell, vol. 48(3), pages 651-653, August.
    2. Dubin, Robin A., 1998. "Spatial Autocorrelation: A Primer," Journal of Housing Economics, Elsevier, vol. 7(4), pages 304-327, December.
    3. 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.
    4. 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.
    5. A. F. Militino & M. D. Ugarte & L. García-Reinaldos, 2004. "Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 193-209, September.
    6. Robin Dubin, 2008. "Comment On “A Further Exploration Into The Robustness Of Spatial Autocorrelation Specifications”," Journal of Regional Science, Wiley Blackwell, vol. 48(3), pages 641-649, August.
    7. 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.
    8. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2007. "Spatial Dependence, Housing Submarkets, and House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 143-160, August.
    9. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
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    Cited by:

    1. Kena Mi & Rulong Zhuang, 2022. "Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China," Sustainability, MDPI, vol. 14(6), pages 1-19, March.

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    More about this item

    Keywords

    Spatial autocorrelation; Specification error; Robustness; Model choice;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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