This paper explores the impact of error-term non-normality on the performance of the normal-error Generalized Autoregressive Conditional Heteroskedastic (GARCH) model under small and moderate sample sizes. A non-normal-, asymmetric-error GARCH model is proposed, and its finite-sample performance...

This paper develops and illustrates the application of a procedure to evaluate and compare the cost effectiveness of alternative crop insurance products for cotton in terms of their effect on expected producer net returns and the variation of net returns. Farm unit-level cotton yields and...

This study presents a technique that can jointly model and simulate the expected values, variances, and covariances of sets of correlated time-series dependent variables that are autocorrelated and non-normal (right or left skewed and/or kurtotic). It illustrates the method by applying it to...

This paper revisits the issue of crop yield distributions using improved model specifications, estimation and testing procedures that address the methodological concerns raised in recent literature that could have invalidated previous conclusions of yield non-normality. It shows beyond...

Threshold autoregressive (TAR) models can accommodate the asymmetric cycling behavior observed in some time series data. This study develops a procedure to estimate TAR models when the conditional mean of the dependent variable is function of one or more exogenous factors while allowing for...

Replaced with revised version of paper 11/28/06. Former title: The Expanded and Re-Parameterized Johnson System: A Most Crop-Yield Distribution Model

Recently developed techniques are adapted and combined for the modeling and simulation of crop yields and prices that can be mutually correlated, exhibit heteroskedasticity or autocorrelation, and follow nonnormal probability density functions. The techniques are applied to the modeling and...

This study analyzes the risks of diversified tropical cropping systems that combine cocoa, plantain, and tree-crop components in different proportions versus traditional monocultures. A technique for modeling the expected values, variances, and covariances of correlated time-series variables...

This paper proposes and explores the use of a partially adaptive estimation technique to improve the reliability of the inferences made from multiple regression models when the dependent variable is not normally distributed. The relevance of this technique for agricultural economics research is...

Recently developed techniques are combined for modeling mutually correlated crop yields and prices that exhibit heteroscedasticity and autocorrelation, respectively, and follow non-normal probability density functions (pdf's). The importance rigorously modeling these pdf's for financial risk...