IDEAS home Printed from https://ideas.repec.org/p/cwl/cwldpp/2313r1.html
   My bibliography  Save this paper

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 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 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.
  • Handle: RePEc:cwl:cwldpp:2313r1
    as

    Download full text from publisher

    File URL: https://cowles.yale.edu/sites/default/files/2023-04/d2313r1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Steve Berry & Oliver B. Linton & Ariel Pakes, 2004. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," Review of Economic Studies, Oxford University Press, vol. 71(3), pages 613-654.
    3. 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.
    4. 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.
    5. Chen Z. & Kuo L., 2001. "A Note on the Estimation of the Multinomial Logit Model With Random Effects," The American Statistician, American Statistical Association, vol. 55, pages 89-95, May.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    12. Liran Einav, 2007. "Seasonality in the U.S. motion picture industry," RAND Journal of Economics, RAND Corporation, vol. 38(1), pages 127-145, March.
    13. 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.
    14. 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.
    15. Peter E. Rossi, 2014. "Bayesian Non- and Semi-parametric Methods and Applications," Economics Books, Princeton University Press, edition 1, number 10259.
    16. Kevin R. Williams, 2022. "The Welfare Effects of Dynamic Pricing: Evidence From Airline Markets," Econometrica, Econometric Society, vol. 90(2), pages 831-858, March.
    17. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    18. 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 2312R3, Cowles Foundation for Research in Economics, Yale University, revised Jan 2023.
    19. 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.
    20. 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)

    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 2313, Cowles Foundation for Research in Economics, Yale University.
    2. 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.
    3. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    4. Mogens Fosgerau & Julien Monardo & André de Palma, 2019. "The Inverse Product Differentiation Logit Model," Working Papers hal-02183411, HAL.
    5. Doi, Naoshi, 2022. "A simple method to estimate discrete-type random coefficients logit models," International Journal of Industrial Organization, Elsevier, vol. 81(C).
    6. 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.
    7. 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.
    8. Freyberger, Joachim, 2015. "Asymptotic theory for differentiated products demand models with many markets," Journal of Econometrics, Elsevier, vol. 185(1), pages 162-181.
    9. Christopher Conlon & Julie Holland Mortimer, 2021. "Empirical properties of diversion ratios," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 693-726, December.
    10. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    11. 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.
    12. Wang, Ao, 2023. "Sieve BLP: A semi-nonparametric model of demand for differentiated products," Journal of Econometrics, Elsevier, vol. 235(2), pages 325-351.
    13. 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.
    14. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.
    15. Laura Grigolon, 2021. "Blurred boundaries: A flexible approach for segmentation applied to the car market," Quantitative Economics, Econometric Society, vol. 12(4), pages 1273-1305, November.
    16. Thomas W. Quan & Kevin R. Williams, 2016. "Product Variety, Across-Market Demand Heterogeneity, and the Value of Online Retail," Cowles Foundation Discussion Papers 2054, Cowles Foundation for Research in Economics, Yale University.
    17. Steven T. Berry & Philip A. Haile, 2009. "Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers," NBER Working Papers 15276, National Bureau of Economic Research, Inc.
    18. Moon, Hyungsik Roger & Shum, Matthew & Weidner, Martin, 2018. "Estimation of random coefficients logit demand models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 613-644.
    19. Dearing, Adam, 2022. "Estimating structural demand and supply models using tax rates as instruments," Journal of Public Economics, Elsevier, vol. 205(C).
    20. 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.

    More about this item

    Keywords

    Discrete Choice Modeling; Demand Estimation; Zero-Sale Observations; Bayesian Methods; 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cwl:cwldpp:2313r1. 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: Brittany Ladd (email available below). General contact details of provider: https://edirc.repec.org/data/cowleus.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.