IDEAS home Printed from https://ideas.repec.org/a/wly/quante/v14y2023i4p1251-1294.html
   My bibliography  Save this article

Demand estimation with infrequent purchases and small market sizes

Author

Listed:
  • Ali Hortaçsu
  • Olivia R. Natan
  • Hayden Parsley
  • Timothy Schwieg
  • Kevin R. Williams

Abstract

We propose a demand estimation method 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 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 as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand.

Suggested Citation

  • Ali Hortaçsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2023. "Demand estimation with infrequent purchases and small market sizes," Quantitative Economics, Econometric Society, vol. 14(4), pages 1251-1294, November.
    • Handle: RePEc:wly:quante:v:14:y:2023:i:4:p:1251-1294
    DOI: 10.3982/QE2147
    as

    Download full text from publisher

    File URL: https://doi.org/10.3982/QE2147
    Download Restriction: no
    File URL: https://libkey.io/10.3982/QE2147?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gustavo Vulcano & Garrett van Ryzin & Richard Ratliff, 2012. "Estimating Primary Demand for Substitutable Products from Sales Transaction Data," Operations Research, INFORMS, vol. 60(2), pages 313-334, April.
    2. Tarek Abdallah & Gustavo Vulcano, 2021. "Demand Estimation Under the Multinomial Logit Model from Sales Transaction Data," Manufacturing & Service Operations Management, INFORMS, vol. 23(5), pages 1196-1216, September.
    3. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82, pages 1749-1797, September.
    4. Steven Berry & Philip Haile, 2016. "Identification in Differentiated Products Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 27-52, October.
    5. 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.
    6. Steve Berry & Oliver B. Linton & Ariel Pakes, 2004. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(3), pages 613-654.
    7. Thomas W. Quan & Kevin R. Williams, 2018. "Product variety, across‐market demand heterogeneity, and the value of online retail," RAND Journal of Economics, RAND Corporation, vol. 49(4), pages 877-913, December.
    8. Christopher T. Conlon & Julie Holland Mortimer, 2013. "Demand Estimation under Incomplete Product Availability," American Economic Journal: Microeconomics, American Economic Association, vol. 5(4), pages 1-30, November.
    9. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    10. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    11. Liran Einav, 2007. "Seasonality in the U.S. motion picture industry," RAND Journal of Economics, RAND Corporation, vol. 38(1), pages 127-145, March.
    12. Amit Gandhi & Zhentong Lu & Xiaoxia Shi, 2023. "Estimating demand for differentiated products with zeroes in market share data," Quantitative Economics, Econometric Society, vol. 14(2), pages 381-418, May.
    13. Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
    14. Aditya Jain & Nils Rudi & Tong Wang, 2015. "Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need," Operations Research, INFORMS, vol. 63(1), pages 134-150, February.
    15. Thomas W. Quan & Kevin R. Williams, 2017. "Product Variety, Across-Market Demand Heterogeneity, and the Value of Online Retail," Cowles Foundation Discussion Papers 2054R3, Cowles Foundation for Research in Economics, Yale University, revised Jun 2018.
    16. Ruxian Wang, 2021. "Technical Note—Consumer Choice and Market Expansion: Modeling, Optimization, and Estimation," Operations Research, INFORMS, vol. 69(4), pages 1044-1056, July.
    17. Kevin R. Williams, 2022. "The Welfare Effects of Dynamic Pricing: Evidence From Airline Markets," Econometrica, Econometric Society, vol. 90(2), pages 831-858, March.
    18. Elisabeth Honka, 2014. "Quantifying search and switching costs in the US auto insurance industry," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 847-884, December.
    19. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    20. Jeffrey P. Newman & Mark E. Ferguson & Laurie A. Garrow & Timothy L. Jacobs, 2014. "Estimation of Choice-Based Models Using Sales Data from a Single Firm," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 184-197, May.
    21. Christopher Conlon & Jeff Gortmaker, 2020. "Best practices for differentiated products demand estimation with PyBLP," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1108-1161, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhentong Lu & Kenichi Shimizu, 2025. "Estimating Discrete Choice Demand Models with Sparse Market-Product Shocks," Staff Working Papers 25-10, Bank of Canada.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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 2313R1, Cowles Foundation for Research in Economics, Yale University, revised Mar 2023.
    2. 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.
    3. Lu, Zhentong & Shi, Xiaoxia & Tao, Jing, 2023. "Semi-nonparametric estimation of random coefficients logit model for aggregate demand," Journal of Econometrics, Elsevier, vol. 235(2), pages 2245-2265.
    4. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    5. Mogens Fosgerau & Julien Monardo & André de Palma, 2024. "The Inverse Product Differentiation Logit Model," American Economic Journal: Microeconomics, American Economic Association, vol. 16(4), pages 329-370, November.
    6. Doi, Naoshi, 2022. "A simple method to estimate discrete-type random coefficients logit models," International Journal of Industrial Organization, Elsevier, vol. 81(C).
    7. Pietro Tebaldi & Alexander Torgovitsky & Hanbin Yang, 2023. "Nonparametric Estimates of Demand in the California Health Insurance Exchange," Econometrica, Econometric Society, vol. 91(1), pages 107-146, January.
    8. Philip G. Gayle & Ying Lin, 2022. "Market effects of new product introduction: Evidence from the brew‐at‐home coffee market," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(3), pages 525-557, August.
    9. Wan, Mingchao & Huang, Yihui & Zhao, Lei & Deng, Tianhu & Fransoo, Jan C., 2018. "Demand estimation under multi-store multi-product substitution in high density traditional retail," European Journal of Operational Research, Elsevier, vol. 266(1), pages 99-111.
    10. Christopher A. Neilson, 2021. "Targeted Vouchers, Competition Among Schools, and the Academic Achievement of Poor Students," Working Papers 2021-48, Princeton University. Economics Department..
    11. Donna, Javier D. & Pereira, Pedro & Trindade, Andre & Yoshida, Renan C., 2020. "Direct-to-Consumer Sales by Manufacturers and Bargaining," MPRA Paper 105773, University Library of Munich, Germany.
    12. Freyberger, Joachim, 2015. "Asymptotic theory for differentiated products demand models with many markets," Journal of Econometrics, Elsevier, vol. 185(1), pages 162-181.
    13. Christopher Conlon & Julie Holland Mortimer, 2021. "Empirical properties of diversion ratios," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 693-726, December.
    14. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    15. Steven T. Berry & Philip A. Haile, 2024. "Nonparametric Identification of Differentiated Products Demand Using Micro Data," Econometrica, Econometric Society, vol. 92(4), pages 1135-1162, July.
    16. Brett Hollenbeck & Kosuke Uetake, 2021. "Taxation and market power in the legal marijuana industry," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 559-595, September.
    17. Wang, Ao, 2023. "Sieve BLP: A semi-nonparametric model of demand for differentiated products," Journal of Econometrics, Elsevier, vol. 235(2), pages 325-351.
    18. Odran Bonnet & Alfred Galichon & Yu-Wei Hsieh & Keith O’Hara & Matt Shum, 2022. "Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 3085-3114.
    19. Hyungsik Roger Moon & Matthew Shum & Martin Weidner, 2017. "Estimation of random coefficients logit demand models with interactive fixed effects," CeMMAP working papers 12/17, Institute for Fiscal Studies.
    20. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:quante:v:14:y:2023:i:4:p:1251-1294. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.