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...

The objective of this study is to evaluate and model the yield risk associatedwith major agricultural commodities in the U.S. We are particularly concernedwith the nonstationary nature of the yield distribution, which primarily arisesbecause of technological progress and changing environmental...

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...

This research develops a mixture regression model that is shown to have advantages over theclassical Tobit model in model fit and predictive tests when data are generated from a two stepprocess. Additionally, the model is shown to allow for flexibility in distributional assumptions while nesting...

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...

We explore the validity of the 2-stage least squares estimator with l_{1}-regularization in both stages, for linear regression models where the numbers of endogenous regressors in the main equation and instruments in the first-stage equations can exceed the sample size, and the regression...

We explore the validity of the 2-stage least squares estimator with l_{1}-regularization in both stages, for linear triangular models where the numbers of endogenous regressors in the main equation and instruments in the first-stage equations can exceed the sample size, and the regression...

We develop simple and non-asymptotically justified methods for hypothesis testing about the coefficients ($\theta^{*}\in\mathbb{R}^{p}$) in the high dimensional generalized regression models where $p$ can exceed the sample size. Given a function $h:\,\mathbb{R}^{p}\mapsto\mathbb{R}^{m}$, we...

We develop simple and non-asymptotically justified methods for hypothesis testing about the coefficients ($\theta^{*}\in\mathbb{R}^{p}$) in the high dimensional (generalized) regression models where $p$ can exceed the sample size $n$. Given a function $h:\,\mathbb{R}^{p}\mapsto\mathbb{R}^{m}$,...

We develop non-asymptotically justified methods for hypothesis testing about the p-dimensional coefficients in (possibly nonlinear) regression models, where the hypotheses can also be nonlinear in the coefficients. Our (nonasymptotic) control on the Type I and Type II errors holds for fixed n...