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Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models

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  • Lei, J.

    (Tilburg University, Center for Economic Research)

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    Abstract

    Abstract: This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum score (SSMS) estimator which consistently estimates the model parameters up to scale. The identification of parameters is obtained, when the disturbances are time-stationary and the explanatory variables vary enough over time along with an exogenous and time-invariant spatial weight matrix. Consistency and asymptotic distribution of the proposed estimator are also derived in the paper. Finally, a Monte Carlo study indicates that the SSMS estimator performs quite well in finite samples.

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    Bibliographic Info

    Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2013-061.

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    Date of creation: 2013
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    Handle: RePEc:dgr:kubcen:2013061

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    Web page: http://center.uvt.nl

    Related research

    Keywords: Spatial Autoregressive Models; Binary Choice; Fixed Effects; Maximum Score Estimation;

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    1. BOLDUC, Denis & FORTIN, Bernard & GORDON, Stephen, 1995. "Multinomial Probit Estimation of Spatially Interdependent Choices: an Empirical Comparison of Two New Techniques," Cahiers de recherche 9508, Université Laval - Département d'économique.
    2. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    3. Brock,W.A. & Durlauf,S.N., 2000. "Discrete choice with social interactions," Working papers 7, Wisconsin Madison - Social Systems.
    4. Jacobs, Jan & Samarina, Anna & Heijnen, Pim & Elhorst, Paul, 2013. "State transfers at different moments in time: A spatial probit approach," Research Report 13006-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    5. Klier, Thomas & McMillen, Daniel P, 2008. "Clustering of Auto Supplier Plants in the United States," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 460-471.
    6. Chamberlain, Gary, 1986. "Asymptotic efficiency in semi-parametric models with censoring," Journal of Econometrics, Elsevier, vol. 32(2), pages 189-218, July.
    7. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
    8. de Jong, R.M., 1995. "Laws of Large Numbers for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 11(02), pages 347-358, February.
    9. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    10. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    11. Horowitz, Joel L., 2002. "Bootstrap critical values for tests based on the smoothed maximum score estimator," Journal of Econometrics, Elsevier, vol. 111(2), pages 141-167, December.
    12. Charlier, G.W.P. & Melenberg, B. & Soest, A.H.O. van, 1995. "A smoothed maximum score estimator for the binary choice panel data model with an application to labour force participation," Open Access publications from Tilburg University urn:nbn:nl:ui:12-365498, Tilburg University.
    13. Jason R. Blevins & Shakeeb Khan, 2013. "Local NLLS estimation of semi‐parametric binary choice models," Econometrics Journal, Royal Economic Society, vol. 16(2), pages 135-160, 06.
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