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Parametric Modeling and Simulation of Joint Price-Production Distributions under Non-Normality, Autocorrelation and Heteroscedasticity: A Tool for Assessing Risk in Agriculture

Published online by Cambridge University Press:  28 April 2015

Octavio A. Ramirez
Octavio A. Ramirez*
Affiliation:
Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, Texas
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Abstract

This study presents a way to parametrically model and simulate multivariate distributions under potential non-normality, autocorrelation and heteroscedasticity and illustrates its application to agricultural risk analysis. Specifically, the joint probability distribution (pdf) for West Texas irrigated cotton, corn, sorghum, and wheat production and prices is estimated and applied to evaluate the changes in the risk and returns of agricultural production in the region resulting from observed and predicted price and production trends. The estimated pdf allows for time trends on the mean and the variance and varying degrees of autocorrelation and non-normality (kurtosis and right- or left-skewness) in each of the price and production variables. It also allows for any possible price-price, production-production, or price-production correlation.

Keywords

agricultural risk analysisautocorrelationheteroscedasticitymultivariate non-normal simulationWest Texas agriculture
Type
Invited Paper Sessions
Information
Journal of Agricultural and Applied Economics , Volume 32 , Issue 2 , August 2000 , pp. 283 - 297
Copyright
Copyright © Southern Agricultural Economics Association 2000

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