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Nonparametric vs Parametric Binary Choice Models: An Empirical Investigation

Christophe Bontemps (), Jeffrey Racine () and Michel Simioni

No 09-126, TSE Working Papers from Toulouse School of Economics (TSE)

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.

Keywords: binary choice models; nonparametric estimation; specification tests (search for similar items in EconPapers)
Date: 2009-12
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Working Paper: Nonparametric vs parametric binary choice models: An empirical investigation (2011) Downloads
Working Paper: Nonparametric vs parametric binary choice models: An empirical investigation (2009) Downloads
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