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Dynamic Demand Estimation in Auction Markets

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  • Matthew Backus
  • Gregory Lewis

Abstract

Economists have developed empirically tractable demand systems for fixed price markets. In contrast, empirical auction techniques treat each auction in isolation, ignoring market interactions. We provide a framework for estimating demand in a large auction market with a dynamic population of buyers with unit demand and heterogeneous preferences over a finite set of differentiated products. We offer an empirically tractable equilibrium concept under which bidders behave as though they are in a steady-state, characterize bidding, and prove existence of equilibrium. Having developed a demand system, we show that it is non-parametrically identified from panel data, and that this result is robust to typical data limitations, reserve prices, random coefficient demand, public signals that refine beliefs about market conditions, unobserved heterogeneity, idiosyncratic preferences, and random latent outside options. We apply the model to estimate demand and measure consumer surplus in the market for compact cameras on eBay. Our analysis highlights the importance of both dynamic bidding strategies and panel data sample selection issues when analyzing these markets.

Suggested Citation

  • Matthew Backus & Gregory Lewis, 2016. "Dynamic Demand Estimation in Auction Markets," NBER Working Papers 22375, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22375
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    Cited by:

    1. Kalai, Ehud & Shmaya, Eran, 2018. "Large strategic dynamic interactions," Journal of Economic Theory, Elsevier, vol. 178(C), pages 59-81.
    2. Kostas Bimpikis & Wedad J. Elmaghraby & Ken Moon & Wenchang Zhang, 2020. "Managing Market Thickness in Online Business-to-Business Markets," Management Science, INFORMS, vol. 66(12), pages 5783-5822, December.
    3. Yoshimoto, Hisayuki & Nakabayashi, Jun, 2019. "Search and resale frictions in a two-sided online platform: A case of multi-use assets," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 85-105.
    4. Yixin Lu & Alok Gupta & Wolfgang Ketter & Eric van Heck, 2019. "Dynamic Decision Making in Sequential Business-to-Business Auctions: A Structural Econometric Approach," Management Science, INFORMS, vol. 65(8), pages 3853-3876, August.

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    More about this item

    JEL classification:

    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L0 - Industrial Organization - - General

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