Estimating a demand system with nonnegativity constraints: Mexican meat demand

• 成果类型: Article
• 署名作者: Golan, A; Perloff, JM; Shen, EZ
• 署名单位: American University; University of California System; University of California Berkeley
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/00346530152480180
• 发表日期: 2001-08
• 页码: 541-550
• 关键词: empirical likelihood; information-theory; regression; models; specification; separability

摘要

A new information-based approach for estimating systems of many equations with nonnegativity constraints is presented. This approach, called generalized maximum entropy (GME), is more practical and efficient than traditional maximum-likelihood methods. The GME method is used to estimate an almost ideal demand system for five types of meat using cross-sectional data from Mexico, where most households did not buy at least one type of meat during the survey week. The system of demands is shown to vary across demographic groups.