Best practices for differentiated products demand estimation with PyBLP
回顾了BLP类型问题估计的最新进展，通过PyBLP包实现通用接口，蒙特卡洛实验表明多个局部最优解在良好识别问题中罕见，小样本下使用最优工具变量和供给端限制也能表现良好。
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 .