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

  • Octavio A. Ramirez (a1), Tanya U. McDonald (a2) and Carlos E. Carpio (a3)


The distributions currently used to model and simulate crop yields are unable to accommodate a substantial subset of the theoretically feasible mean-variance-skewness-kurtosis (MVSK) hyperspace. Because these first four central moments are key determinants of shape, the available distributions might not be capable of adequately modeling all yield distributions that could be encountered in practice. This study introduces a system of distributions that can span the entire MVSK space and assesses its potential to serve as a more comprehensive parametric crop yield model, improving the breadth of distributional choices available to researchers and the likelihood of formulating proper parametric models.



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Anderson, J.R.Simulation: Methodology and Application in Agricultural Economics.Review of Marketing and Agricultural Economics 42(1974):355.
Coble, K.H., Knight, T.O., Pope, R.D., and Williams, J.R.Modeling Farm-level Crop Insurance Demand with Panel Data.American Journal of Agricultural Economics 78(1996):439–47.
Gallagher, P.U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances.American Journal of Agricultural Economics 69(1987):796803.
Johnson, N.L.System of Frequency Curves Generated by Method of Translation.Bio-metrika 36(1949):149–76.
Ker, A.P., and Coble, K.Modeling Conditional Yield Densities.American Journal of Agricultural Economics 85(2003):291304.
Mood, A.M., Graybill, F.A., and Boes, D.C.Introduction to the Theory of Statistics. 3rd ed. New York: McGraw-Hill, 1974.
Moss, C.B., and Shonkwiler, J.S.Estimating Yield Distributions Using a Stochastic Trend Model and Non-Normal Errors.American Journal of Agricultural Economics 75(1993):105662.
Nelson, CH., and Preckel, P.V.The Conditional Beta Distribution as a Stochastic Production Function.American Journal of Agricultural Economics 71(1989):370–78.
Norwood, B., Roberts, M.C., and Lusk, J.L.Ranking Crop Yield Models Using Out-of-Sample Likelihood Functions.American Journal of Agricultural Economics 86(2004):103243.
Ramirez, O.A.Estimation of a Multivariate Parametric Model for Simulating Heteroskedastic, Correlated, Non-Normal Random Variables: The Case of Corn-Belt Corn, Soybeans and Wheat Yields.American Journal of Agricultural Economics 79(1997):191205.
Ramirez, O.A. and McDonald, T.U.Ranking Crop Yield Models: A Comment.” American Journal of Agricultural Economics 88(2006):110510.
Ramirez, O.A., Misra, S.K., and Field, J.E.Crop Yield Distributions Revisited.American Journal of Agricultural Economics 85(2003):108–20.
Ramirez, O.A., Moss, C.B., and Boggess, W.G.Estimation and Use of the Inverse Hyperbolic Sine Transformation to Model Non-Normal Correlated Random Variables.Journal of Applied Statistics 21(1994):289305.
Taylor, CR.Two Practical Procedures for Estimating Multivariate Non-Normal Probability Density Functions.American Journal of Agricultural Economics 72(1990):210–17.
Yatchew, A.Nonparametric Regression Techniques in Economics.Journal of Economic Literature 36(1998):669721.


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

  • Octavio A. Ramirez (a1), Tanya U. McDonald (a2) and Carlos E. Carpio (a3)


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