PARAMETRIC MODELING AND SIMULATION OF JOINT PRICE-PRODUCTION DISTRIBUTIONS UNDER NON-NORMALITY, AUTOCORRELATION AND HETEROSCEDASTICITY: A TOOL FOR ASSESSING RISK IN AGRICULTURE
Journal of Agricultural and Applied Economics, 2000, vol. 32, issue 2, 15
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: Research Methods/ Statistical Methods; Risk and Uncertainty (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Journal Article: Parametric Modeling and Simulation of Joint Price-Production Distributions under Non-Normality, Autocorrelation and Heteroscedasticity: A Tool for Assessing Risk in Agriculture (2000)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ags:joaaec:15486
Access Statistics for this article
More articles in Journal of Agricultural and Applied Economics from Southern Agricultural Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().