Best practices for differentiated products demand estimation with PyBLP
成果类型：
Article
署名作者：
Conlon, Christopher; Gortmaker, Jeff
署名单位：
New York University; Harvard University
刊物名称：
RAND JOURNAL OF ECONOMICS
ISSN/ISSBN：
0741-6261
发表日期：
2020
页码：
1108-1161
关键词：
摘要：
Differentiated products demand systems are a workhorse for understanding the price effects of mergers, the value of new goods, and the contribution of products to seller networks. Berry, Levinsohn, and Pakes (1995) provide a flexible random coefficients logit model which accounts for the endogeneity of prices. This article reviews and combines several recent advances related to the estimation of BLP-type problems and implements an extensible generic interface via the PyBLP package. Monte Carlo experiments and replications suggest different conclusions than the prior literature: multiple local optima appear to be rare in well-identified problems; good performance is possible even in small samples, particularly when optimal instruments are employed along with supply-side restrictions. If Python is installed on your computer, PyBLP can be installed with the following command: pip install pyblp.Up-to-date documentation for the package is available at https://pyblp.readthedocs.io.