Bontemps, Christophe, Racine, Jeffrey S. and Simioni, Michel (2009) Nonparametric vs Parametric Binary Choice Models: An Empirical Investigation. TSE Working Paper, n. 09-126

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Abstract

The estimation of conditional probability distribution functions (PDFs) in a kernel nonparametric framework has recently received attention. As emphasized by Hall, Racine & 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 & 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.