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A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions AgEcon
Ramirez, Octavio A.; McDonald, Tanya U.; Carpio, Carlos E..
The distributions currently used to model and simulate crop yields are unable to accommodate a substantial subset of the theoretically feasible mean-variance-skewness-kurtosis (MVSK) hyperspace. Because these first four central moments are key determinants of shape, the available distributions might not be capable of adequately modeling all yield distributions that could be encountered in practice. This study introduces a system of distributions that can span the entire MVSK space and assesses its potential to serve as a more comprehensive parametric crop yield model, improving the breadth of distributional choices available to researchers and the likelihood of formulating proper parametric models.
Tipo: Journal Article Palavras-chave: Risk analysis; Parametric methods; Yield distributions; Yield modeling and simulation; Yield nonnormality; Agribusiness; Agricultural Finance; Crop Production/Industries; Land Economics/Use; Production Economics; Productivity Analysis; Research Methods/ Statistical Methods; C15; C16; C46; C63.
Ano: 2010 URL: http://purl.umn.edu/90675
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Evaluating Yield Models for Crop Insurance Rating AgEcon
Lanoue, Christopher; Sherrick, Bruce J.; Woodard, Joshua D.; Paulson, Nicholas D..
Generated crop insurance rates depend critically on the distributional assumptions of the underlying crop yield loss model. Using farm level corn yield data from 1972-2008, we revisit the problem of examining in-sample goodness-of-fit measures across a set of flexible parametric, semi-parametric, and non-parametric distributions. Simulations are also conducted to investigate the out-of-sample efficiency properties of several competing distributions. The results indicate that more parameterized distributional forms fit the data better in-sample due to the fact that they have more parameters, but are generally less efficient out-of-sample–and in some cases more biased–than more parsimonious forms which also fit the data adequately, such as the Weibull. The...
Tipo: Conference Paper or Presentation Palavras-chave: Yield distributions; Crop Insurance; Weibull Distribution; Beta Distribution; Mixture Distribution; Out-of-Sample Efficiency; Goodness-of-Fit; Insurance Rating Efficiency; Farm Management; Financial Economics; Land Economics/Use.
Ano: 2010 URL: http://purl.umn.edu/61761
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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
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