IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v27y2018icp19-36.html
   My bibliography  Save this article

A model for broad choice data

Author

Listed:
  • Brownstone, David
  • Li, Phillip

Abstract

This paper analyzes a discrete choice model where the observed outcome is not the exact alternative chosen by a decision maker but rather the broad group of alternatives which contain the chosen alternative. The model is designed for situations where the choice behavior at a particular level is of interest but only broader level data are available. For example, consider analyzing a household's choice for a vehicle at the make-model-trim level but only choice data at the make-model level are observed. The proposed model is a generalization of the multinomial logit model and collapses to it when there is full observability of the exact choices. We show that the parameters in the model are at least locally identified, but for certain configurations of the data, they are only weakly identified. Methods to address weak identification are proposed when there are data available on the overall market shares of all alternatives, and both maximum likelihood and Bayesian estimation methods are explored.

Suggested Citation

  • Brownstone, David & Li, Phillip, 2018. "A model for broad choice data," Journal of choice modelling, Elsevier, vol. 27(C), pages 19-36.
    • Handle: RePEc:eee:eejocm:v:27:y:2018:i:c:p:19-36
    DOI: 10.1016/j.jocm.2017.09.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534517300878
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2017.09.001?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. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    3. Chib, Siddhartha & Ramamurthy, Srikanth, 2010. "Tailored randomized block MCMC methods with application to DSGE models," Journal of Econometrics, Elsevier, vol. 155(1), pages 19-38, March.
    4. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    5. Wong, Timothy & Brownstone, David & Bunch, David S., 2019. "Aggregation biases in discrete choice models," Journal of choice modelling, Elsevier, vol. 31(C), pages 210-221.
    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. Domarchi, Cristian & Cherchi, Elisabetta, 2024. "Role of car segment and fuel type in the choice of alternative fuel vehicles: A cross-nested logit model for the English market," Applied Energy, Elsevier, vol. 357(C).
    2. Lloro, Alicia & Brownstone, David, 2018. "Vehicle choice and utilization: Improving estimation with partially observed choices and hybrid pairs," Journal of choice modelling, Elsevier, vol. 28(C), pages 137-152.

    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. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    2. Dunker, Fabian & Hoderlein, Stefan & Kaido, Hiroaki, 2014. "Nonparametric Identification of Endogenous and Heterogeneous Aggregate Demand Models: Complements, Bundles and the Market Level," Economics Series 307, Institute for Advanced Studies.
    3. Timothy B. Armstrong & Michal Kolesár, 2021. "Sensitivity analysis using approximate moment condition models," Quantitative Economics, Econometric Society, vol. 12(1), pages 77-108, January.
    4. Friberg, Richard & Romahn, André, 2015. "Divestiture requirements as a tool for competition policy: A case from the Swedish beer market," International Journal of Industrial Organization, Elsevier, vol. 42(C), pages 1-18.
    5. Verboven, Frank & Bourreau, Marc & Sun, Yutec, 2018. "Market Entry, Fighting Brands and Tacit Collusion: The Case of the French Mobile Telecommunications Market," CEPR Discussion Papers 12866, C.E.P.R. Discussion Papers.
    6. 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.
    7. Doi, Naoshi & Ohashi, Hiroshi, 2017. "Empirical analysis of the national treatment obligation under the WTO: The case of Japanese shochu," Journal of the Japanese and International Economies, Elsevier, vol. 46(C), pages 43-52.
    8. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    9. Farasat A.S. Bokhari & Franco Mariuzzo & Weijie Yan, 2019. "Antibacterial resistance and the cost of affecting demand: the case of UK antibiotics," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2019-03, Centre for Competition Policy, University of East Anglia, Norwich, UK..
    10. Juan Esteban Carranza & Alejandra Ximena González, 2014. "Estimación de la demanda de vehículos nuevos de los hogares colombianos entre 2001 y 2011," Borradores de Economia 824, Banco de la Republica de Colombia.
    11. David P. Byrne & Susumu Imai & Vasilis Sarafidis, 2015. "Instrument-free Identifcation and Estimation of the Diferentiated Products Models," Department of Economics - Working Papers Series 1198, The University of Melbourne.
    12. Anindya Ghose & Sang Pil Han, 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science, INFORMS, vol. 60(6), pages 1470-1488, June.
    13. Rancière, Romain & Ouazad, Amine, 2015. "Structural Demand Estimation with Borrowing Constraints," CEPR Discussion Papers 10866, C.E.P.R. Discussion Papers.
    14. Fabian Dunker & Konstantin Eckle & Katharina Proksch & Johannes Schmidt-Hieber, 2017. "Tests for qualitative features in the random coefficients model," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 225, Courant Research Centre PEG.
    15. Zhu, Chen & Lopez, Rigoberto A. & Liu, Xiaoou, 2019. "Consumer responses to front-of-package labeling in the presence of information spillovers," Food Policy, Elsevier, vol. 86(C), pages 1-1.
    16. 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.
    17. 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.
    18. 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.
    19. Reynaert, Mathias & Verboven, Frank, 2014. "Improving the performance of random coefficients demand models: The role of optimal instruments," Journal of Econometrics, Elsevier, vol. 179(1), pages 83-98.
    20. Lee, Jinhyuk & Seo, Kyoungwon, 2016. "Revisiting the nested fixed-point algorithm in BLP random coefficients demand estimation," Economics Letters, Elsevier, vol. 149(C), pages 67-70.

    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:eejocm:v:27:y:2018:i:c:p:19-36. 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.journals.elsevier.com/journal-of-choice-modelling .

    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.