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Spatial Effects in Probit Models: A Monte Carlo Investigation

In: New Directions in Spatial Econometrics

Author

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  • Daniel P. McMillen

    (Tulane University)

Abstract

Heteroscedasticity and autocorrelation typically are assumed to be absent in econometric models. Linear regression models are forgiving if these assumptions fail: ordinary least squares (OLS) estimates remain consistent if errors are not homoscedastic or are autocorrelated. Estimators for models with discrete data are not always as forgiving as OLS. For example, the Standard probit estimator continues to provide consistent estimates when error terms are autocorrelated, but the estimates are inconsistent as well as inefficient when errors have non-constant variances. Failure of the homoscedasticity assumption also leads to inconsistent estimates in such common models as tobit and logit. Thus, heteroscedasticity is a serious problem in models with discrete data.

Suggested Citation

  • Daniel P. McMillen, 1995. "Spatial Effects in Probit Models: A Monte Carlo Investigation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax (ed.), New Directions in Spatial Econometrics, chapter 9, pages 189-228, Springer.
    • Handle: RePEc:spr:adspcp:978-3-642-79877-1_9
    DOI: 10.1007/978-3-642-79877-1_9
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    Citations

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    Cited by:

    1. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
    2. Feng Li & Guangdong Li & Weishan Qin & Jing Qin & Haitao Ma, 2018. "Identifying Economic Growth Convergence Clubs and Their Influencing Factors in China," Sustainability, MDPI, vol. 10(8), pages 1-21, July.
    3. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.
    4. Andrea Amaral & Margarida Abreu & Victor Mendes, 2014. "The Spatial Probit Model – An Application to the Study of Banking Crises at the End of the 90’s," CEFAGE-UE Working Papers 2014_05, University of Evora, CEFAGE-UE (Portugal).
    5. Angel Alañon-Pardo & Patrick J. Walsh & Rafael Myro, 2018. "Do neighboring municipalities matter in industrial location decisions? Empirical evidence from Spain," Empirical Economics, Springer, vol. 55(3), pages 1145-1179, November.
    6. Angel Alanón & Josep Maria Arauzo Carod, "undated". "Accessibility and Industrial Location. Some Evidence from Spain," Studies on the Spanish Economy 214, FEDEA.
    7. Amaral, Andrea & Abreu, Margarida & Mendes, Victor, 2014. "The spatial Probit model—An application to the study of banking crises at the end of the 1990’s," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 251-260.
    8. Wang, Xiaokun (Cara) & Kockelman, Kara M. & Lemp, Jason D., 2012. "The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data," Journal of Transport Geography, Elsevier, vol. 24(C), pages 77-88.
    9. Angel Alañón Pardo & Josep Maria Arauzo Carod, 2009. "Accessibility and Industrial Location: evidence from Spain," Documentos de trabajo de la Facultad de Ciencias Económicas y Empresariales 09-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
    10. Bhat, Chandra, 1999. "An analysis of evening commute stop-making behavior using repeated choice observations from a multi-day survey," Transportation Research Part B: Methodological, Elsevier, vol. 33(7), pages 495-510, September.
    11. Ozturk, Erdogan & Irwin, Elena G., 2001. "Explaining Household Location Choices Using A Spatial Probit Model," 2001 Annual meeting, August 5-8, Chicago, IL 20626, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Ángel Alanón & Rafael Myro, "undated". "Does neighboring "industrial atmosphere" matter in industrial location?. Empirical evidence from Spanish municipalities," Studies on the Spanish Economy 199, FEDEA.
    13. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    14. Wang, Honglin & Iglesias, Emma M. & Wooldridge, Jeffrey M., 2013. "Partial maximum likelihood estimation of spatial probit models," Journal of Econometrics, Elsevier, vol. 172(1), pages 77-89.
    15. Raymond J. G. M. Florax & Arno J. Van der Vlist, 2003. "Spatial Econometric Data Analysis: Moving Beyond Traditional Models," International Regional Science Review, , vol. 26(3), pages 223-243, July.

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