Estimation of Censored Demand Equations from Large Cross-Section Data

Federico Perali () and Jean-Paul Chavas ()

American Journal of Agricultural Economics, 2000, vol. 82, issue 4, pages 1022-37

Abstract: The article develops an alternative econometric methodology to estimate a system of censored demand equations using a large cross-section data from Colombian urban households. The approach preserves the behavioral information expressed by zero expenditures and conforms with the requirements imposed by consumer theory in a way consistent with the random utility hypothesis. We motivate the choice of the Tobit model as a statistical representation of consumer behavior and introduce the methodology by specifying the AIDS model modified according to both a translating and scaling demographic transformation. We propose to estimate each demand equation in unrestricted form using the jackknife technique. We then recover the demand parameters imposing the cross-equations restrictions by using minimum distance estimation. The empirical results of the censored demand system for specific households of policy relevance are reported. Copyright 2000 by American Agricultural Economics Association

Date: 2000
View citations in EconPapers

Downloads: (external link)
http://www.blackwell-synergy.com/servlet/useragent ... &year=2000&part=null link to full text (text/html)
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.

Persistent link: http://EconPapers.repec.org/RePEc:bla:ajagec:v:82:y:2000:i:4:p:1022-37

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0002-9092

Access Statistics for this article

American Journal of Agricultural Economics is edited by Peter Berck, Robert J. Myers, Ian M. Sheldon and B. Wade Brorsen

More articles in American Journal of Agricultural Economics from American Agricultural Economics Association
Series data maintained by Christopher F. Baum ().