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	Provedor de dados AgEcon (2) Autor Ker, Alan P. (1) Racine, Jeffrey S. (1) Schnitkey, Gary D. (1) Sherrick, Bruce J. (1) Woodard, Joshua D. (1) Palavra-chave Insurance rating (2) Risk and Uncertainty (2) Actuarially fair (1) Crop Production/Industries (1) Crop insurance (1) Discrete data (1) Kernel estimation (1) Loss cost ratio (1) Risk Management Agency (1) Risk growth (1) Yield distributions (1) Yield trends (1) Mais... Tipo do documento Journal Article (2) Ano 2011 (1) 2006 (1) País United States (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Actuarial Impacts of Loss Cost Ratio Ratemaking in U.S. Crop Insurance Programs AgEcon Woodard, Joshua D.; Sherrick, Bruce J.; Schnitkey, Gary D.. This study examines the actuarial implications of the loss cost ratio (LCR) ratemaking methodology employed by the Risk Management Agency as a component of base rates for U.S. crop insurance programs, and identifies specific conditions required for the LCR methodology to result in unbiased rates when liabilities trend. Specifically, constant relative yield risk resulting in growing absolute variance through time and other restrictive requirements are required for the LCR to result in unbiased rates. These requirements are tested against a large farm-level data set for Illinois corn. Our findings indicate that the conditions required for appropriate use of the LCR methodology are violated for this high premium volume market, resulting in large implied rate... Tipo: Journal Article Palavras-chave: Actuarially fair; Crop insurance; Insurance rating; Loss cost ratio; Risk growth; Risk Management Agency; Yield trends; Crop Production/Industries; Risk and Uncertainty. Ano: 2011 URL: http://purl.umn.edu/105550 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: 2 Primeira ... 1 ... Última

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