Incorporating Micro Data into Differentiated Products Demand Estimation with PyBLP
本摘要源自该文的 NBER 工作论文版（2023），正式发表版可能有调整。
We delineate a general framework for incorporating many types of micro data from summary statistics to full surveys of selected consumers into Berry, Levinsohn, and Pakes (1995)-style estimates of differentiated products demand systems. We extend recommended practices for BLP estimation in Conlon and Gortmaker (2020) to the case with micro data and implement them in our open-source package PyBLP. Monte Carlo experiments and empirical examples suggest that incorporating micro data can substantially improve the finite sample performance of the BLP estimator, particularly when using well-targeted summary statistics or “optimal micro moments” that we derive and show how to compute.