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Estimating Nonlinearities in Spatial Autoregressive Models

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  • Nicolas Debarsy

    ()
    (CERPE - Centre de Recherches en Economie Régionale et Politique Economique - Université de Namur)

  • Vincenzo Verardi

    ()
    (European Centre for Advanced Research in Economics and Statistics (ECARES) - Université Libre de Bruxelles, CRED - Centre de Recherche en Economie du Développement - Université de Namur)

Abstract

In spatial autoregressive models, the functional form of autocorrelation is assumed to be linear. In this paper, we propose a simple semiparametric procedure, based on Yatchew's (1998) partial linear least squares, that relaxes this restriction. Simple simulations show that this model outperforms traditional SAR estimation when nonlinearities are present. We then apply the methodology on real data to test for the spatial pattern of voting for independent candidates in US presidential elections. We find that in some counties, votes for “third candidates” are non-linearly related to votes for “third candidates” in neighboring counties, which pleads for strategic behavior.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00446574.

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Date of creation: 13 Jan 2010
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Handle: RePEc:hal:wpaper:halshs-00446574

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Keywords: Spatial econometrics; semiparametric estimations;

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  1. Whitney Newey & James Powell & Francis Vella, 1998. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Working papers 98-16, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
  3. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  4. G�ran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
  5. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
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