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Incorporating Micro Data into Differentiated Products Demand Estimation with PyBLP

Author

Listed:
  • Christopher Conlon
  • Jeff Gortmaker

Abstract

We provide 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 best 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.

Suggested Citation

  • Christopher Conlon & Jeff Gortmaker, 2023. "Incorporating Micro Data into Differentiated Products Demand Estimation with PyBLP," NBER Working Papers 31605, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31605
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    Cited by:

    1. Tianyu Du & Ayush Kanodia & Susan Athey, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Papers 2304.01906, arXiv.org, revised Jul 2023.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L0 - Industrial Organization - - General
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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