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Nonparametric vs parametric binary choice models: An empirical investigation

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  • Bontemps, Christophe
  • Racine, Jeffrey S.
  • Simioni, Michel

Abstract

The estimation of conditional probability distribution functions (PDFs) in a kernel nonparametric framework has recently received attention. As emphasized by Hall, Racine and Li (2004), these conditional PDFs are extremely useful for a range of tasks including modelling and predicting consumer choice. The aim of this paper is threefold. First, we implement nonparametric kernel estimation of PDF with a binary choice variable and both continuous and discrete explanatory variables. Second, we address the issue of the performances of this nonparametric estimator when compared to a classic on-the-shelf parametric estimator, namely a probit. We propose to evaluate these estimators in terms of their predictive performances, in the line of the recent "revealed performance" test proposed by Racine and Parmeter (2009). Third, we provide a detailed discussion of the results focusing on environmental insights provided by the two estimators, revealing some patterns that can only be detected using the nonparametric estimator.
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Suggested Citation

  • Bontemps, Christophe & Racine, Jeffrey S. & Simioni, Michel, 2011. "Nonparametric vs parametric binary choice models: An empirical investigation," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 116005, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae11:116005
    DOI: 10.22004/ag.econ.116005
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    References listed on IDEAS

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    1. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, October.
    3. Bontemps, Christophe & Nauges, Celine, 2010. "Carafe ou bouteille ? Le rôle de la qualité de l’environnement dans la décision du consommateur," INRAE Sciences Sociales, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2), vol. 2010, pages 1-4, September.
    4. BONTEMPS Christophe & NAUGES Céline, 2006. "Carafe ou bouteille ? Le rôle de la qualité de l'environnement dans la décision du consommateur," LERNA Working Papers 06.07.200, LERNA, University of Toulouse.
    5. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    6. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
    7. Briesch R.A. & Chintagunta P.K. & Matzkin R.L., 2002. "Semiparametric Estimation of Brand Choice Behavior," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 973-982, December.
    8. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    9. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
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    Cited by:

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    2. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    3. Levon Demirdjian & Majid Mojirsheibani, 2019. "Kernel classification with missing data and the choice of smoothing parameters," Statistical Papers, Springer, vol. 60(5), pages 1487-1513, October.

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