Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T18:51:05.284Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  26 January 2015

Octavio A. Ramirez ,
Tanya U. McDonald  and
Carlos E. Carpio
Octavio A. Ramirez
Affiliation:
Department of Agricultural and Applied Economics, University of Georgia, Athens, GA
Tanya U. McDonald
Affiliation:
Department of Agricultural Economics and Agricultural Business, New Mexico State University, Las Cruces, NM
Carlos E. Carpio
Affiliation:
Department of Applied Economics and Statistics, Clemson University, Clemson, SC
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

risk analysisparametric methodsyield distributionsyield modeling and simulationyield nonnormalityC15C16C46C63
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 42 , Issue 2 , May 2010 , pp. 303 - 319
Copyright
Copyright © Southern Agricultural Economics Association 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J.R. “Simulation: Methodology and Application in Agricultural Economics.” Review of Marketing and Agricultural Economics 42(1974):3–55.Google Scholar
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.CrossRefGoogle Scholar
Gallagher, P. “U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances.” American Journal of Agricultural Economics 69(1987):796–803.CrossRefGoogle Scholar
Johnson, N.L. “System of Frequency Curves Generated by Method of Translation.” Bio-metrika 36(1949):149–76.Google ScholarPubMed
Ker, A.P., and Coble, K. “Modeling Conditional Yield Densities.” American Journal of Agricultural Economics 85(2003):291–304.CrossRefGoogle Scholar
Mood, A.M., Graybill, F.A., and Boes, D.C.Introduction to the Theory of Statistics. 3rd ed. New York: McGraw-Hill, 1974.Google Scholar
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):1056–62.CrossRefGoogle Scholar
Nelson, CH., and Preckel, P.V. “The Conditional Beta Distribution as a Stochastic Production Function.” American Journal of Agricultural Economics 71(1989):370–78.CrossRefGoogle Scholar
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):1032–43.CrossRefGoogle Scholar
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):191–205.CrossRefGoogle Scholar
Ramirez, O.A. and McDonald, T.U. “Ranking Crop Yield Models: A Comment.” American Journal of Agricultural Economics 88(2006):1105–10.CrossRefGoogle Scholar
Ramirez, O.A., Misra, S.K., and Field, J.E. “Crop Yield Distributions Revisited.” American Journal of Agricultural Economics 85(2003):108–20.CrossRefGoogle Scholar
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):289–305.CrossRefGoogle Scholar
Taylor, CR. “Two Practical Procedures for Estimating Multivariate Non-Normal Probability Density Functions.” American Journal of Agricultural Economics 72(1990):210–17.CrossRefGoogle Scholar
Yatchew, A. “Nonparametric Regression Techniques in Economics.” Journal of Economic Literature 36(1998):669–721.Google Scholar