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,

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,

Evaluation of Crop Insurance Yield Guarantees and Producer Welfare with Upward Trending Yields

Actual Production History (APH) yields play a critical role in determining the coverage offered to producers by the Risk Management Agency’s (RMA) Yield Protection, Revenue Protection, and Revenue Protection-Harvest Price Exclusion crop insurance products. The RMA currently uses the simple average of from 4 to 10 years of historical yields to determine the APH yield guarantee. If crop yields are trending upward, use of a simple average of historical yields introduces bias into the insurance offering. Using both county and individual insured unit data, we examine the producer impact of APH yield trends for Texas cotton and Illinois corn. Our findings indicate that biases due to using simple average APH yields when yields are trending upward reduce the expected indemnity and actuarially fair premium rate. Certainty equivalent differences are computed and used as a measure of the magnitude of welfare effect of trend-based biases in APH yields. The estimated welfare effect also varies significantly with different commonly used detrending approaches. This study demonstrates that producer welfare can be enhanced through proper treatment of yield trends in crop insurance programs.Actual Production History, Crop Insurance, Yield Trend, Yield Guarantee, Production Economics, Risk and Uncertainty,

YIELD GUARANTEES AND THE PRODUCER WELFARE BENEFITS OF CROP INSURANCE

Crop yield and revenue insurance products with coverage based on actual production history (APH) yields dominate the U.S. Federal Crop Insurance Program. The APH yield, which plays a critical role in determining the coverage offered to producers, is based on a small sample of historical yields for the insured unit. The properties of this yield measure are critical in determining the value of the insurance to producers. Sampling error in APH yields has the potential to lead to over-insurance in some years and under-insurance in other years. Premiums, which are in part determined by the ratio of the APH yield to the county reference yield, are also affected by variations in APH yields. Congress has enacted two measures, yield substitution and yield floors, that are intended to limit the degree to which sampling error can reduce the insurance guarantee and producer welfare. We examine the impact of sampling error and related policy provisions for Texas cotton, Kansas wheat, and Illinois corn. The analysis is conducted using county level yield data from the National Agricultural Statistics Service and individual insured-unit-level yield data obtained from the Risk Management Agency’s insurance database. Our findings indicate that sampling error in APH yields has the potential to reduce producer welfare and that the magnitude of this effect differs substantially across crops. The yield substitution and yield floor provisions reduce the negative impact of sampling error but also bias guarantees upward, leading to increased government cost of the insurance programs.Actual Production History, Crop Insurance, Sampling Error, Yield Guarantee, Production Economics, Risk and Uncertainty,

Is There Evidence that Legislative Ambition Matches Development? Evaluating the Factors Influencing Wind Energy Development

Resource /Energy Economics and Policy,

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,

Quality Risk and Profitability in Cattle Production: A Multivariate Approach

This study evaluates quality, production, and price risk within the context of overall profit variability in fed cattle production. The approach used offers a flexible way to estimate a large system of equations with more than three jointly related censored outcomes. Trade-offs between quality and yield grade levels and production measures, such as average daily gain and feeding efficiency, are evaluated. Simulation procedures are used to assess the impact of quality risk on overall profit variability. Results make an important contribution to existing research by explaining why price signals through grid quality grade premiums may not generate intended producer responses.censoring, copula, fed cattle, grid pricing, multivariate, quality risk, Livestock Production/Industries,

Probabilistic Models of Yield, Price, and Revenue Risks for Fed Cattle Production

Livestock Production/Industries,

Consumer Heterogeneity: Does It Affect Policy Responses to the Obesity Epidemic?

The fight against obesity in the U.S. has become a priority area for policy makers due to the additional health risks and health care costs. In developing policy to lower obesity rates, it is important to accurately characterize the impact that exercise, smoking and demographic characteristics have on BMI in order to draft effective policy. This analysis uses data from the Behavioral Risk Factor Surveillance System (BRFS) to evaluate the relationship between behavioral and demographic factors with BMI while explicitly accounting for individual heterogeneity by using a quantile analysis. Results suggest that the effect of exercise, smoking, occupation and race vary by BMI quantile, indicating that consumers should be treated as heterogeneous at least for these factors in obesity policy and related analyses.Obesity, Quantile Regression, Heterogeneity, Policy, Food Consumption/Nutrition/Food Safety, I18,