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Incorporating Search and Sales Information in Demand Estimation

Author

Listed:
  • Ali Hortacsu

    (University of Chicago and NBER)

  • Olivia R. Natan

    (University of California, Berkeley)

  • Hayden Parsley

    (University of Texas, Austin)

  • Timothy Schwieg

    (University of Chicago, Booth)

  • Kevin R. Williams

    (Cowles Foundation, Yale University)

Abstract

We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data and measures of consumer search intensity. After presenting simulation studies, we consider an empirical application of air travel demand where product-level sales are sparse. We find considerable variation in demand over time. Periods of peak demand feature both larger market sizes and consumers with higher willingness to pay. This amplifies cyclicality. However, observed frequent price and capacity adjustments offset some of this compounding effect.

Suggested Citation

  • Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Incorporating Search and Sales Information in Demand Estimation," Cowles Foundation Discussion Papers 2313, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:2313
    as

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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d23/d2313.pdf
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    References listed on IDEAS

    as
    1. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers 2312, Cowles Foundation for Research in Economics, Yale University.
    2. Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
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    More about this item

    Keywords

    Discrete Choice Modeling; Demand Estimation; Zeros; Bayesian Methods; Cyclical Demand; Airline Markets;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation

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