Estimation and Use of a Multivariate Parametric Model for Simulating Heteroskedastic, Correlated, Nonnormal Random Variables: The Case of Corn Belt Corn, Soybean, and Wheat Yields

Octavio Ramirez

American Journal of Agricultural Economics, 1997, vol. 79, issue 1, 191-205

Abstract: This study develops a multivariate, nonnormal density function that can accurately and separately account for skewness, kurtosis, heteroskedasticity, and the correlation among the random variables of interest. The statistical attributes of the underlying random variables and correlation processes are examined. The potential applications of this modeling tool are discussed and exemplified by analyzing and simulating Corn Belt corn, soybean, and wheat yields. While corn and soybean yields are found to be skewed and kurtotic and exhibit different variances through time, wheat yields appear normal but also heteroskedastic. A strong correlation is detected between corn and soybean yields. Copyright 1997, Oxford University Press.

Date: 1997
References: Add references at CitEc
Citations: View citations in EconPapers (54)

Downloads: (external link)
http://hdl.handle.net/10.2307/1243953 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:79:y:1997:i:1:p:191-205

Access Statistics for this article

American Journal of Agricultural Economics is currently edited by Madhu Khanna, Brian E. Roe, James Vercammen and JunJie Wu

More articles in American Journal of Agricultural Economics from Agricultural and Applied Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().