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Optimal Estimation of Discrete Choice Demand Models with Consumer and Product Data

Author

Listed:
  • Paul L. E. Grieco
  • Charles Murry
  • Joris Pinkse
  • Stephan Sagl

Abstract

We propose a conformant likelihood estimator with exogeneity restrictions (CLEER) for random coefficients discrete choice demand models that is applicable in a broad range of data settings. It combines the likelihoods of two mixed logit estimators—one for consumer level data, and one for product level data—with product level exogeneity restrictions. Our estimator is both efficient and conformant: its rates of convergence will be the fastest possible given the variation available in the data. The researcher does not need to pre-test or adjust the estimator and the inference procedure is valid across a wide variety of scenarios. Moreover, it can be tractably applied to large datasets. We illustrate the features of our estimator by comparing it to alternatives in the literature.

Suggested Citation

  • Paul L. E. Grieco & Charles Murry & Joris Pinkse & Stephan Sagl, 2025. "Optimal Estimation of Discrete Choice Demand Models with Consumer and Product Data," NBER Working Papers 33397, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:33397
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    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
    • L0 - Industrial Organization - - General

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