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Demand Analysis with Many Prices

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Listed:
  • Victor Chernozhukov
  • Jerry A. Hausman
  • Whitney K. Newey

Abstract

From its inception, demand estimation has faced the problem of "many prices." This paper provides estimators of average demand and associated bounds on exact consumer surplus when there are many prices in cross-section or panel data. For cross-section data we provide a debiased machine learner of consumer surplus bounds that allows for general heterogeneity and solves the "zeros problem" of demand. For panel data we provide bias corrected, ridge regularized estimators of average coefficients and consumer surplus bounds. In scanner data we find smaller panel elasticities than cross-section and that soda price increases are regressive.

Suggested Citation

  • Victor Chernozhukov & Jerry A. Hausman & Whitney K. Newey, 2019. "Demand Analysis with Many Prices," NBER Working Papers 26424, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26424
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Pierre Dubois & Rachel Griffith & Martin O'Connell, 2020. "How Well Targeted Are Soda Taxes?," American Economic Review, American Economic Association, vol. 110(11), pages 3661-3704, November.
    4. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    5. O'Connell, Martin & Smith, Kate, 2020. "Corrective Tax Design and Market Power," CEPR Discussion Papers 14582, C.E.P.R. Discussion Papers.
    6. Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Martin O'Connell & Kate Smith, 2024. "Optimal Sin Taxation and Market Power," American Economic Journal: Applied Economics, American Economic Association, vol. 16(4), pages 34-70, October.
    8. Ming Li, 2021. "Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model," Papers 2110.00982, arXiv.org, revised Nov 2024.
    9. Lin, Xirong, 2023. "Food demand and cash transfers: A collective household approach with Homescan data," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 233-259.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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