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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; 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.; 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 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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| 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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| 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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