Ranking Crop Yield Models: A Comment

Abstract

This comment discusses key specification issues that may have affected the performance and, therefore, the ranking of parametric models that were compared in a recent AJAE article. A procedure to obtain the most flexible parametric model specification possible, given the particular probability distribution function on which the model is based is presented. These specifications also allow for standardized and, therefore, more valid comparisons across parametric models that are based on different probability distributions. Finally, the comment cautions against generalization of the rankings in that AJAE article and recommends that these more flexible specifications be adopted in future comparisons and applications. Copyright 2006, Oxford University Press.

Heterogeneity and Distributional Form of Farm‐Level Yields

“…33This study has also been criticized for other reasons (Ramirez and McDonald 2006). Other studies that have attempted to infer yield distributions from county‐level data are by Taylor (1990), Ramirez (1997), and Harri et al (2008).…”

Can Crop Insurance Premiums Be Reliably Estimated?

“…This system, which is composed of the Su and the SB families (Johnson 1949), can accommodate any mean-variance-skewness-kurtosis (MVSK) combination that might be encountered in practice (Ramirez, McDonald, and Carpio 2010, Ramirez and McDonald 2006a, Ramirez, Misra, and Field 2003. This property makes the Johnson system preferable for use in this research to other less flexible distributions such as the Beta or Gamma which allow for only very limited MVSK combinations (Ramirez and McDonald 2006a). Another advantage of using Ramirez, McDonald, and Carpio (2010) results is that they identify a variety of distributional shapes that span over a substantial area of the theoretically feasible skewness-kurtosis (SK) space.'…”

“…Specifically, the analysis is based on yield distributions previously estimated on the basis of one of the most comprehensive farm-level datasets in the United States (Sherrick et al 2004) and recently developed parametric modeling procedures. Since these procedures are flexible enough to accommodate a wide variety of distributional shapes (Ramirez, McDonald, and Carpio 2010, Ramirez and McDonald 2006a, Ramirez, Misra, and Field 2003, the estimated distributions should sufficiently resemble the true underlying yield densities to make the analyses realistic. The estimated distributions are then assumed to be the true data-generating processes and used to simulate yield datasets for the analyses.…”

A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions

“…However, in the parameterizations proposed by Johnson, each of those SK combinations is arbitrarily associated with a fixed set of meanvariance values. Ramirez and McDonald (2006) outline a reparameterization technique that expands any probability distribution by two parameters that specifically and uniquely control the mean and variance without affecting the range of skewness and kurtosis values that can be accommodated. The expanded distribution obtained through this reparameterization can therefore model any conceivable mean and variance in conjunction with the set of SK combinations allowed by the original distribution.…”