Best practices for differentiated products demand estimation with PyBLP

Abstract

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. ∗Thanks to Steve Berry, Jeremy Fox, Phil Haile, Mathias Reynaert, and Frank Verboven and seminar participants at NYU, Rochester, and the 2019 IIOC conference. Thanks to the editor Marc Rysman and to three anonymous referees. Daniel Stackman provided excellent research assistance. Any remaining errors are our own. †New York University, Stern School of Business: cconlon@stern.nyu.edu ‡Harvard University: jgortmaker@g.harvard.edu