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Robustness of Spatial Autocorrelation Specifications: Some Monte Carlo Evidence

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  • Robin Dubin

Abstract

This paper examines the robustness of various models of spatial autocorrelation through a series of Monte Carlo experiments in which each model takes a turn at the data generator. The generated data are then used to estimate all of the models. The estimated models are evaluated primarily on their predictive power.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jregsc:v:43:y:2003:i:2:p:221-248
    DOI: 10.1111/1467-9787.00297
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    Cited by:

    1. Gallaher, Adam & Graziano, Marcello & Fiaschetti, Maurizio, 2021. "Legacy and shockwaves: A spatial analysis of strengthening resilience of the power grid in Connecticut," Energy Policy, Elsevier, vol. 159(C).
    2. Daniel Lo & Kwong Wing Chau & Siu Kei Wong & Michael McCord & Martin Haran, 2022. "Factors Affecting Spatial Autocorrelation in Residential Property Prices," Land, MDPI, vol. 11(6), pages 1-16, June.
    3. Yong Tu & Seow Ong & Ying Han, 2009. "Turnovers and Housing Price Dynamics: Evidence from Singapore Condominium Market," The Journal of Real Estate Finance and Economics, Springer, vol. 38(3), pages 254-274, April.
    4. Marko Kryvobokov, 2011. "Defining apartment neighbourhoods with Thiessen polygons and fuzzy equality clustering," ERES eres2011_142, European Real Estate Society (ERES).
    5. 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.
    6. Zengwang Xu & Robert Harriss, 2010. "A Spatial and Temporal Autocorrelated Growth Model for City Rank—Size Distribution," Urban Studies, Urban Studies Journal Limited, vol. 47(2), pages 321-335, February.
    7. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "The relative efficiencies of various predictors in spatial econometric models containing spatial lags," Regional Science and Urban Economics, Elsevier, vol. 37(3), pages 363-374, May.
    8. Dean M Hanink, 2007. "Spatial and Geographical Effects in Regional Multiplier Analysis," Environment and Planning A, , vol. 39(3), pages 748-762, March.
    9. Jesus Mur & Ana Angulo, 2005. "A closer look at the Spatial Durbin Model," ERSA conference papers ersa05p392, European Regional Science Association.
    10. Paula Margaretic & Christine Thomas-Agnan & Romain Doucet, 2017. "Spatial dependence in (origin-destination) air passenger flows," Papers in Regional Science, Wiley Blackwell, vol. 96(2), pages 357-380, June.
    11. 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.
    12. Helen R. Neill & David M. Hassenzahl & Djeto D. Assane, 2007. "Estimating the Effect of Air Quality: Spatial versus Traditional Hedonic Price Models," Southern Economic Journal, John Wiley & Sons, vol. 73(4), pages 1088-1111, April.
    13. 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.
    14. Yong Tu & Helen X.H. Bao, 2009. "Property Rights and Housing Value: The Impacts of Political Instability," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(2), pages 235-257, June.
    15. Kato, Takafumi, 2012. "Prediction in the lognormal regression model with spatial error dependence," Journal of Housing Economics, Elsevier, vol. 21(1), pages 66-76.
    16. Kato, Takafumi, 2013. "A comparison of spatial error models through Monte Carlo experiments," Economic Modelling, Elsevier, vol. 30(C), pages 743-753.

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