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| Registros recuperados: 10 | |
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| Belasco, Eric J.; Goodwin, Barry K.; Ghosh, Sujit K.. |
| The purpose of this study is to evaluate the risks faced by fed cattle producers. With the development of livestock insurance programs as part of the Agricultural Risk Protection Act of 2000, a thorough investigation into the probabilistic measures of individual risk factors is needed. This research jointly models cattle production yield risk factors, using a multivariate dynamic regression model. A multivariate framework is necessary to characterize yield risk in terms of four yield factors (dry matter feed conversion, averaged daily gain, mortality, and veterinary costs), which are highly correlated. Additionally, a conditional Tobit model is used to handle censored yield variables (e.g., mortality). The proposed econometric model estimates... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Livestock Production/Industries. |
| Ano: 2007 |
URL: http://purl.umn.edu/9382 |
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| Zhu, Ying; Goodwin, Barry K.; Ghosh, Sujit K.. |
| The objective of this study is to evaluate and model the yield risk associated with major agricultural commodities in the U.S. We are particularly concerned with the nonstationary nature of the yield distribution, which primarily arises because of technological progress and changing environmental conditions. Precise risk assessment depends on the accuracy of modeling this distribution. This problem becomes more challenging as the yield distribution changes over time, a condition that holds for nearly all major crops. A common approach to this problem is based on a two-stage method in which the yield is first detrended and then the estimated residuals are treated as observed data and modeled using various parametric or nonparametric methods. We propose an... |
| Tipo: Working or Discussion Paper |
Palavras-chave: Crop Insurance; Model Comparison; Time-Varying Distribution; Financial Economics. |
| Ano: 2011 |
URL: http://purl.umn.edu/102048 |
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| Ozaki, Vitor Augusto; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo. |
| This article focuses on the modeling of agricultural yield data using hierarchical Bayesian models. In recovering the generating process of these data, we consider the temporal, spatial and spatio-temporal relationships pertinent to the prediction and pricing of insurance contracts based on regional crop yields. A county-average yield data set was analyzed for the State of Paraná, Brazil for the period of 1990 through 2002. The choice of the best model from among the several non-nested models considered was based on the posterior predictive criterion. The methodology used in this article proposes improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Research Methods/ Statistical Methods. |
| Ano: 2005 |
URL: http://purl.umn.edu/19142 |
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| Zhu, Ying; Goodwin, Barry K.; Ghosh, Sujit K.. |
| The objective of this study is to evaluate and model the yield risk associated with major agricultural commodities in the U.S. We are particularly concerned with the nonstationary nature of the yield distribution, which primarily arises because of technological progress and changing environmental conditions. Precise risk assessment depends on the accuracy of modeling this distribution. This problem becomes more challenging as the yield distribution changes over time, a condition that holds for nearly all major crops. A common approach to this problem is based on a two-stage method in which the yield is first detrended and then the estimated residuals are treated as observed data and modeled using various parametric or nonparametric methods. We propose an... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Risk and Uncertainty. |
| Ano: 2011 |
URL: http://purl.umn.edu/104420 |
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| Zhu, Ying; Ghosh, Sujit K.; Goodwin, Barry K.. |
| The objective of this study is to evaluate and model the spatial dependence of systemic yield risk. Various spatial autoregressive models are explored to account for county level dependence of crop yields. The results show that the time trend parameters of yields are correlated across spaces and the spatial correlations are changing with time. In addition, the spatial correlation of neighborhood in west/east direction is stronger than that of north/south direction. The information of the spatial dependence of yield risk will help the construction of better risk management programs for protecting producers from systemic yield risks. |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Spatial Autoregressive Model; Spatial Dependence; Risk and Uncertainty. |
| Ano: 2009 |
URL: http://purl.umn.edu/49455 |
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| Zhu, Ying; Goodwin, Barry K.; Ghosh, Sujit K.. |
| The objective of this study is to evaluate the risk associated with major agricultural commodity yields in the United States. We are particularly concerned with the nonstationary nature of the yield distribution, which arises primarily as a result of technological progress and changing environmental conditions over time. In contrast to common two-stage methods, we propose an alternative parametric model that allows the moments of yield distributions to change with time. Several model selection techniques suggest the proposed time-varying model outperforms more conventional models in terms of in-sample goodness-of-fit, out-of-sample predictive power, and the prediction accuracy of insurance premium rates. |
| Tipo: Journal Article |
Palavras-chave: Crop insurance; Model comparison; Time-varying distribution; Crop Production/Industries; Risk and Uncertainty. |
| Ano: 2011 |
URL: http://purl.umn.edu/105549 |
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| Zhu, Ying; Ghosh, Sujit K.. |
| The objective of this study is to evaluate the robust regression method when detrending the crop yield data. Using a Monte Carlo simulation method, the performance of the proposed Time-Varying Beta method is compared with the previous study of OLS, M-estimator and MM-estimator in an application of crop yield modeling. We analyze the properties of these estimators for outlier-contaminated data in both symmetric and skewed distribution case. The application of these estimation methods is illustrated in an agricultural insurance analysis. The consequence of obtaining more accurate detrending method will offer the potential to improve the accuracy of models used in rating crop insurance contracts. |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Research Methods/ Statistical Methods; Risk and Uncertainty. |
| Ano: 2011 |
URL: http://purl.umn.edu/103426 |
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| Zhu, Ying; Ghosh, Sujit K.; Goodwin, Barry K.. |
| The objective of this study is to evaluate and model the risks of corn and soybean production. This study focuses on the risk of revenue variability that arises from changes in prices, yields shortfalls or both. There are several models for price and yield risk factors for corn and soybeans. For instance, yield risks can be modeled by a family of Beta distributions, whereas price shocks can be modeled by log-normal distributions. In order to develop a multivariate model that preserves a given set of marginals, a copula approach can be used to characterize the joint yield and price risk of corn and soybeans, which are usually highly correlated. The copula approach has been spurred by the recent developments in the whole farm insurance (WFI), resulting in... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Copula; Crop Insurance; Loss Distribution; Farm Management. |
| Ano: 2008 |
URL: http://purl.umn.edu/6282 |
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| Belasco, Eric J.; Ghosh, Sujit K.. |
| This research develops a mixture regression model that is shown to have advantages over the classical Tobit model in model fit and predictive tests when data are generated from a two step process. Additionally, the model is shown to allow for flexibility in distributional assumptions while nesting the classic Tobit model. A simulated data set is utilized to assess the potential loss in efficiency from model misspecification, assuming the Tobit and a zero-inflated log-normal distribution, which is derived from the generalized mixture model. Results from simulations key on the finding that the proposed zero-inflated log-normal model clearly outperforms the Tobit model when data are generated from a two step process. When data are generated from a Tobit... |
| Tipo: Conference Paper or Presentation |
Palavras-chave: Censoring; Livestock production; Tobit; Zero-inflated; Bayesian; Livestock Production/Industries. |
| Ano: 2008 |
URL: http://purl.umn.edu/6341 |
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| Registros recuperados: 10 | |
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