期刊名称：BRE : Brazilian Review of Econometrics / Sociedade Brasileira de Econometria
印刷版ISSN：0101-7012
出版年度：2006
卷号：26
期号：2
页码：212-234
出版社：Rio de Janeiro
摘要：This paper builds on the methodology developed by Katayma, Lu and Tybout (2003),
who use a nested logit demand model to estimate demand parameters from plant-level
data that usually report only revenue and cost figures. I demonstrate how to extend their
framework by including the extra information provided by commonly available data on
aggregate physical output. Using data from the Colombian beer industry from 1977 to
1990, the model, estimated through Bayesian Monte Carlo Methods, shows a sizeable
precision gain in the parameter estimates once the aggregate variable is included.
