Household food expenditures in the United States: A Bayesian MCMC approach to censored equation systems

Panagiotis P. Kasteridis and Steven T. Yen

No 61763, 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado from Agricultural and Applied Economics Association

Abstract: We apply a Bayesian Markov Chain Monte Carlo (MCMC) technique, along with data augmentation to accommodate censoring in the dependent variables, to the estimation of a large expenditure system of food expenditures. Our finding of significant error covariance estimates justifies estimation of the system in improving statistical efficiency. Income, household composition, regions and other socio-demographic variables are found to play significant roles in determining household food expenditures.

Keywords: Food; Consumption/Nutrition/Food; Safety (search for similar items in EconPapers)
Pages: 24
Date: 2010-07
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/61763/files/kasteridis_yen_aaea10.pdf (application/pdf)

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:ags:aaea10:61763

DOI: 10.22004/ag.econ.61763

Access Statistics for this paper

More papers in 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado from Agricultural and Applied Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().