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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
    1. Chernozhukov, Victor & Fernández-Val, Iván & Newey, Whitney K., 2019. "Nonseparable multinomial choice models in cross-section and panel data," Journal of Econometrics, Elsevier, vol. 211(1), pages 104-116.
    2. Yuichi Kitamura & Jörg Stoye, 2018. "Nonparametric Analysis of Random Utility Models," Econometrica, Econometric Society, vol. 86(6), pages 1883-1909, November.
    3. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    4. 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.
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    14. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    15. Blundell, Richard & Kristensen, Dennis & Matzkin, Rosa, 2014. "Bounding quantile demand functions using revealed preference inequalities," Journal of Econometrics, Elsevier, vol. 179(2), pages 112-127.
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    20. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    23. Soren Blomquist & Whitney Newey, 2002. "Nonparametric Estimation with Nonlinear Budget Sets," Econometrica, Econometric Society, vol. 70(6), pages 2455-2480, November.
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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 Oct 2020.
    2. 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.

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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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