Revisiting Aggregate U.S. Meat Demand with a Bayesian Averaging of Classical Estimates Approach: Do We Need a More General Theory?
Henry L. Bryant and
American Journal of Agricultural Economics, 2008, vol. 90, issue 1, 103-116
Although meat demand is one of the most studied issues in agricultural economics, our understanding of this phenomenon has been hampered by valid concerns about model specification uncertainty. This article revisits the need for more general theories of aggregate U.S. meat demand. Using a Bayesian averaging of classical estimates approach, we draw comprehensive inferences over 1,048,576 demand systems. We find very little evidence supporting the need for more general theories that include demand determinants beyond prices and expenditures. We find strong evidence in support of symmetry and negativity, but strong evidence against homogeneity, which is consistent with other research. Copyright 2008, Oxford University Press.
References: Add references at CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
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:oup:ajagec:v:90:y:2008:i:1:p:103-116
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 ().