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	Provedor de dados AgEcon (3) Autor Racine, Jeffrey S. (3) Bontemps, Christophe (2) Simioni, Michel (2) Ker, Alan P. (1) Palavra-chave Research Methods/ Statistical Methods (2) Binary choice models (1) Discrete data (1) Insurance rating (1) Kernel estimation (1) Nonparametric estimation (1) Risk and Uncertainty (1) Specification test (1) Tap water demand (1) Yield distributions (1) Mais... Tipo do documento Conference Paper or Presentation (2) Journal Article (1) Ano 2011 (1) 2009 (1) 2006 (1) País United States (3) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última Nonparametric vs parametric binary choice models: An empirical investigation AgEcon Bontemps, Christophe; Racine, Jeffrey S.; Simioni, Michel. 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... Tipo: Conference Paper or Presentation Palavras-chave: Binary choice models; Nonparametric estimation; Specification test; Tap water demand; Research Methods/ Statistical Methods. Ano: 2009 URL: http://purl.umn.edu/49286 Nonparametric vs parametric binary choice models: An empirical investigation AgEcon Bontemps, Christophe; Racine, Jeffrey S.; Simioni, Michel. Tipo: Conference Paper or Presentation Palavras-chave: Research Methods/ Statistical Methods. Ano: 2011 URL: http://purl.umn.edu/116005 Rating Crop Insurance Policies with Efficient Nonparametric Estimators that Admit Mixed Data Types AgEcon Racine, Jeffrey S.; Ker, Alan P.. The identification of improved methods for characterizing crop yield densities has experienced a recent surge in activity due in part to the central role played by crop insurance in the Agricultural Risk Protection Act of 2000 (estimates of yield densities are required for the determination of insurance premium rates). Nonparametric kernel methods have been successfully used to model yield densities; however, traditional kernel methods do not handle the presence of categorical data in a satisfactory manner and have therefore tended to be applied on a county-by-county basis. By utilizing recently developed kernel methods that admit mixed data types, we are able to model the yield density jointly across counties, leading to substantial finite sample... Tipo: Journal Article Palavras-chave: Discrete data; Insurance rating; Kernel estimation; Yield distributions; Risk and Uncertainty. Ano: 2006 URL: http://purl.umn.edu/10146 Registros recuperados: 3 Primeira ... 1 ... Última

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