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.

中文翻译：

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使用PyBLP进行差异化产品需求估算的最佳实践

差异化的产品需求系统是了解合并的价格影响，新产品的价值以及产品对卖方网络的贡献的主要动力。Berry，Levinsohn和Pakes（1995）提供了一种灵活的随机系数logit模型，该模型说明了价格的内生性。本文回顾并结合了与BLP类型问题估计有关的最新进展，并通过PyBLP包实现了可扩展的通用接口。蒙特卡洛实验和重复实验得出的结论与现有文献不同：在公认的问题中，很少有多个局部最优解；即使在小样本中，也可以实现良好的性能，尤其是在使用“最佳仪器”以及供应方限制的情况下。如果您的计算机上安装了Python，则可以使用以下命令安装PyBLP：pip install pyblp。有关该软件包的最新文档，请访问https://pyblp.readthedocs.io。