IDEAS home Printed from https://ideas.repec.org/a/eee/indorg/v81y2022ics0167718722000017.html
   My bibliography  Save this article

A simple method to estimate discrete-type random coefficients logit models

Author

Listed:
  • Doi, Naoshi

Abstract

This paper proposes a new method for estimating random coefficients logit models using aggregate data. The method analytically obtains the value of the econometric error term and thus does not require numerical calculations, in contrast to the contraction mapping established by Berry et al. (1995). The proposed approach drastically reduces the computation time and is applicable for models with discrete-type heterogeneity in consumer tastes. The approach requires additional data on total sales for each consumer type, though such data do not have to be observed at the product-level. This data requirement implies that the method mainly captures observed heterogeneity.

Suggested Citation

  • Doi, Naoshi, 2022. "A simple method to estimate discrete-type random coefficients logit models," International Journal of Industrial Organization, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:indorg:v:81:y:2022:i:c:s0167718722000017
    DOI: 10.1016/j.ijindorg.2022.102825
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167718722000017
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijindorg.2022.102825?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jean‐Pierre Dubé & Jeremy T. Fox & Che‐Lin Su, 2012. "Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation," Econometrica, Econometric Society, vol. 80(5), pages 2231-2267, September.
    2. Randy Brenkers & Frank Verboven, 2006. "Liberalizing A Distribution System: The European Car Market," Journal of the European Economic Association, MIT Press, vol. 4(1), pages 216-251, March.
    3. Steven Berry & Michael Carnall & Pablo T. Spiller, 1996. "Airline Hubs: Costs, Markups and the Implications of Customer Heterogeneity," NBER Working Papers 5561, National Bureau of Economic Research, Inc.
    4. Alon Eizenberg & Alberto Salvo, 2015. "The Rise of Fringe Competitors in the Wake of an Emerging Middle Class: An Empirical Analysis," American Economic Journal: Applied Economics, American Economic Association, vol. 7(3), pages 85-122, July.
    5. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82(5), pages 1749-1797, September.
    6. 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.
    7. Myrto Kalouptsidi, 2012. "From market shares to consumer types: Duality in differentiated product demand estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 333-342, March.
    8. Federico Ciliberto & Jonathan W. Williams, 2014. "Does multimarket contact facilitate tacit collusion? Inference on conduct parameters in the airline industry," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 764-791, December.
    9. David Besanko & Jean-Pierre Dubé & Sachin Gupta, 2003. "Competitive Price Discrimination Strategies in a Vertical Channel Using Aggregate Retail Data," Management Science, INFORMS, vol. 49(9), pages 1121-1138, September.
    10. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    11. Jinhyuk Lee & Kyoungwon Seo, 2015. "A computationally fast estimator for random coefficients logit demand models using aggregate data," RAND Journal of Economics, RAND Corporation, vol. 46(1), pages 86-102, March.
    12. Laura Grigolon & Frank Verboven, 2014. "Nested Logit or Random Coefficients Logit? A Comparison of Alternative Discrete Choice Models of Product Differentiation," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 916-935, December.
    13. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 239-292, September.
    14. Steven Berry & Panle Jia, 2010. "Tracing the Woes: An Empirical Analysis of the Airline Industry," American Economic Journal: Microeconomics, American Economic Association, vol. 2(3), pages 1-43, August.
    15. Toshiaki Iizuka, 2007. "Experts' agency problems: evidence from the prescription drug market in Japan," RAND Journal of Economics, RAND Corporation, vol. 38(3), pages 844-862, September.
    16. 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.
    17. Keane, Michael P. & Wasi, Nada, 2016. "How to model consumer heterogeneity? Lessons from three case studies on SP and RP data," Research in Economics, Elsevier, vol. 70(2), pages 197-231.
    18. Amil Petrin, 2002. "Quantifying the Benefits of New Products: The Case of the Minivan," Journal of Political Economy, University of Chicago Press, vol. 110(4), pages 705-729, August.
    19. Guido W. Imbens & Tony Lancaster, 1994. "Combining Micro and Macro Data in Microeconometric Models," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 655-680.
    20. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    21. Matthew Backus & Christopher Conlon & Michael Sinkinson, 2021. "Common Ownership and Competition in the Ready-to-Eat Cereal Industry," NBER Working Papers 28350, National Bureau of Economic Research, Inc.
    22. Cardell, N. Scott, 1997. "Variance Components Structures for the Extreme-Value and Logistic Distributions with Application to Models of Heterogeneity," Econometric Theory, Cambridge University Press, vol. 13(2), pages 185-213, April.
    23. Doi, Naoshi & Ohashi, Hiroshi, 2019. "Market structure and product quality: A study of the 2002 Japanese airline merger," International Journal of Industrial Organization, Elsevier, vol. 62(C), pages 158-193.
    24. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    25. Brunner, Daniel & Heiss, Florian & Romahn, André & Weiser, Constantin, 2017. "Reliable estimation of random coefficient logit demand models," DICE Discussion Papers 267, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    26. Naoshi Doi, 2022. "Choice of Policy Instruments with Endogenous Quality: Per‐Passenger and Per‐Flight Airport Charges in Japan," Journal of Industrial Economics, Wiley Blackwell, vol. 70(1), pages 44-88, March.
    27. 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. Kandelhardt, Johannes, 2023. "Flexible estimation of random coefficient logit models of differentiated product demand," DICE Discussion Papers 399, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).

    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. 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.
    2. Mogens Fosgerau & Julien Monardo & André de Palma, 2019. "The Inverse Product Differentiation Logit Model," Working Papers hal-02183411, HAL.
    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. Bokhari, Farasat A.S. & Mariuzzo, Franco, 2018. "Demand estimation and merger simulations for drugs: Logits v. AIDS," International Journal of Industrial Organization, Elsevier, vol. 61(C), pages 653-685.
    5. Miravete, Eugenio J. & Seim, Katja & Thurk, Jeff, 2023. "Pass-through and tax incidence in differentiated product markets," International Journal of Industrial Organization, Elsevier, vol. 90(C).
    6. , 2023. "Price Competition and Endogenous Product Choice in Networks: Evidence from the US airline Industry," Working Papers 950, Queen Mary University of London, School of Economics and Finance.
    7. Xavier D’Haultfœuille & Isis Durrmeyer & Philippe Février, 2019. "Automobile Prices in Market Equilibrium with Unobserved Price Discrimination," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(5), pages 1973-1998.
    8. 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.
    9. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics, Canadian Economics Association, vol. 50(5), pages 1445-1488, December.
    10. Gautam Gowrisankaran & Marc Rysman, 2012. "Dynamics of Consumer Demand for New Durable Goods," Journal of Political Economy, University of Chicago Press, vol. 120(6), pages 1173-1219.
    11. Matthew Backus & Christopher Conlon & Michael Sinkinson, 2021. "Common Ownership and Competition in the Ready-to-Eat Cereal Industry," NBER Working Papers 28350, National Bureau of Economic Research, Inc.
    12. Kandelhardt, Johannes, 2023. "Flexible estimation of random coefficient logit models of differentiated product demand," DICE Discussion Papers 399, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    13. 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.
    14. C. Luke Watson & Oren Ziv, 2021. "Is the Rent Too High? Land Ownership and Monopoly Power," CESifo Working Paper Series 8864, CESifo.
    15. 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.
    16. Donna, Javier D. & Pereira, Pedro & Pires, Tiago & Trindade, Andre, 2018. "Measuring the Welfare of Intermediaries in Vertical Markets," MPRA Paper 90465, University Library of Munich, Germany.
    17. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    18. Satoshi Myojo & Yuichiro Kanazawa, 2010. "On Asymptotic Properties of the Parameters of Differentiated Product Demand and Supply Systems When Demographically-Categorized Purchasing Pattern Data are Available," Discussion Papers 1009, Graduate School of Economics, Kobe University.
    19. Javier Donna & Andre Trindade & Pedro Pereira & Tiago Pires, 2018. "Measuring the Welfare of Intermediation in Vertical Markets," 2018 Meeting Papers 984, Society for Economic Dynamics.
    20. Malina, Christiane, 2016. "The environmental impact of vehicle circulation tax reform in Germany," CAWM Discussion Papers 86, University of Münster, Münster Center for Economic Policy (MEP).

    More about this item

    Keywords

    Demand estimation; Random-coefficient discrete choice model; Latent class model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    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:eee:indorg:v:81:y:2022:i:c:s0167718722000017. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505551 .

    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.