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Demand analysis with many prices

Author

Listed:
  • Victor Chernozhukov

    (Institute for Fiscal Studies and MIT)

  • Jerry Hausman

    (Institute for Fiscal Studies and MIT)

  • Whitney K. Newey

    (Institute for Fiscal Studies and MIT)

Abstract

From its inception, demand estimation has faced the problem of “many prices.” While some aggregation across goods is always necessary, the problem of many prices remains even after aggregation. Although objects of interest may mostly depend on a few prices, many prices should be included to control for omitted variables bias. This paper uses Lasso to mitigate the curse of dimensionality in estimating the aver-age expenditure share from cross-section data. We estimate bounds on consumer surplus (BCS) using a novel double/debiased Lasso method. These bounds allow for general, multidimensional, nonseparable heterogeneity and solve the "zeros problem" of demand by including zeros in the estimation. We also use panel data to allow for prices paid to be correlated with preferences. We average ridge regression individual slope estimators and bias correct for the ridge regularization. We ?nd that panel estimates of price elasticities are much smaller than cross section elasticities in the scanner data we consider. Thus, it is very important to allow correlation of prices and preferences to correctly estimate elasticities. We ?nd less sensitivity of consumer surplus bounds to this correlation.

Suggested Citation

  • Victor Chernozhukov & Jerry Hausman & Whitney K. Newey, 2019. "Demand analysis with many prices," CeMMAP working papers CWP59/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:59/19
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    References listed on IDEAS

    as
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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 Dec 2021.
    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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