A Robust Study of Regression Methods for Crop Yield Data

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.Research Methods/ Statistical Methods, Risk and Uncertainty,

Modeling Censored Data Using Mixture Regression Models with an Application to Cattle Production Yields

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 model, forecasts are more accurate when utilizing the Tobit model. However, the Tobit model will be shown to be a special case of the generalized mixture model. The empirical model is then applied to evaluating mortality rates in commercial cattle feedlots, both independently and as part of a system including other performance and health factors. This particular application is hypothesized to be more appropriate for the proposed model due to the high degree of censoring and skewed nature of mortality rates. The zero-inflated log-normal model clearly models and predicts with more accuracy that the tobit model.censoring, livestock production, tobit, zero-inflated, bayesian, Livestock Production/Industries,

Time-varying Yield Distributions and the U.S. Crop Insurance Program

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 alternative parametric model that allows the moments of the yield distributions to change with time. Several model selection techniques suggest that 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.Risk and Uncertainty,

Directional Spatial Dependence and Its Implications for Modeling Systemic Yield Risk

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.Spatial Autoregressive Model, Spatial Dependence, Risk and Uncertainty,

A Multivariate Evaluation of Ex-ante Risks Associated with Fed Cattle Production

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 parameters that influence the mean and variance of each production yield factor, as well as the covariance between variables. Following the model fitting using a maximum likelihood approach, simulation methods allow for profits, revenue, and gross margins to be evaluated given different assumptions concerning volatility among other shocks. The profit function is composed of random draws, based on conditioning variables, as well as parameter estimates. Shocks to variability, yield factors, or prices allow for a visual representation of the vulnerability of cattle feeder profits to these shocks.Livestock Production/Industries,

An Evaluation of the Soda Tax with Multivariate Nonparametric Regressions

This research extends past work by Shonkwiler and Yen (1999) by allowing for distributional flexibility and nonlinear responses in the form of established semiparametric and nonparametric regressions. The proposed models are shown to outperform the parametric version typically used in demand analysis to characterize a system of censored equations in terms of model fit and prediction power. Using the developed models, we derive elasticities associated with different individual-specific scenarios with regard to the recently proposed “penny-an-ounce” tax on soft drinks sweetened with sugar.censoring, health taxes, nonparametric regressions, Research Methods/ Statistical Methods,