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Provedor de dados:  31
País:  United States
Título:  Nonparametric vs parametric binary choice models: An empirical investigation
Autores:  Bontemps, Christophe
Racine, Jeffrey S.
Simioni, Michel
Data:  2009-04-30
Ano:  2009
Palavras-chave:  Binary choice models
Nonparametric estimation
Specification test
Tap water demand
Research Methods/ Statistical Methods
Resumo:  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.
Tipo:  Conference Paper or Presentation
Idioma:  Inglês
Identificador:  Agricultural & Applied Economics Association Selected Paper N° 611468.

http://purl.umn.edu/49286
Relação:  Agricultural and Applied Economics Association>2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin
Selected Paper
611468
Formato:  18
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