Efficient Estimation of Agricultural Time Series Models with Nonnormal Dependent Variables

Sukant K. Misra and Jeannie Nelson
Authors registered in the RePEc Author Service: Octavio A. Ramirez

American Journal of Agricultural Economics, 2003, vol. 85, issue 4, 1029-1040

Abstract: This article proposes using an expanded form of the Johnson S U family as a way to approximate nonnormal distributions in regression models. The distribution is one of the few that allows modeling heteroskedasticity and autocorrelation. The technique is evaluated with Monte Carlo simulation and illustrated through an empirical model of the West Texas cotton basis. Given nonnormality, this technique can substantially reduce the variance of slope parameter estimates relative to least squares procedures. Copyright 2003, Oxford University Press.

Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
http://hdl.handle.net/10.1111/1467-8276.00505 (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:85:y:2003:i:4:p:1029-1040

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 ().