IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/26449.html
   My bibliography  Save this paper

A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach

Author

Listed:
  • Zenetti, German

Abstract

In this note on the paper from (Jiang, Manchanda & Rossi 2009) I want to discuss a simple alternative estimation method of the multinomial logit model for aggregated data, the so called BLP model, named after (Berry, Levinsohn & Pakes 1995). The estimation is conducted through a bayesian estimation similar to (Jiang et al. 2009). But in difference to them here the time intensive contraction mapping for assessing the mean utility in every iteration step of the estimation procedure is not needed. This is because the likelihood function is computed via a special case of the control function method ((Petrin & Train 2002) and (Park & Gupta 2009)) and hence a full random walk MCMC algorithm is applied. In difference to (Park & Gupta 2009) the uncorrelated error, which is explicitly introduced through the control function procedure, is not integrated out, but sampled with a random walk MCMC. The introduced proceeding enables to use the whole information from the data set in the estimation and beyond that accelerates the computation.

Suggested Citation

  • Zenetti, German, 2010. "A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach," MPRA Paper 26449, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:26449
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/26449/1/MPRA_paper_26449.pdf
    File Function: original version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/26913/1/MPRA_paper_26913.pdf
    File Function: revised version
    Download Restriction: no
    File URL: https://mpra.ub.uni-muenchen.de/27474/1/MPRA_paper_27474.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    4. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    5. Kenneth Train ., 2000. "Halton Sequences for Mixed Logit," Economics Working Papers E00-278, University of California at Berkeley.
    6. 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.
    7. Michelle Sovinsky Goeree, 2008. "Limited Information and Advertising in the U.S. Personal Computer Industry," Econometrica, Econometric Society, vol. 76(5), pages 1017-1074, September.
    8. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    9. Heiss, Florian & Winschel, Viktor, 2006. "Estimation with Numerical Integration on Sparse Grids," Discussion Papers in Economics 916, University of Munich, Department of Economics.
    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. 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.
    2. German Zenetti & Thomas Otter, 2014. "Bayesian estimation of the random coefficients logit from aggregate count data," Quantitative Marketing and Economics (QME), Springer, vol. 12(1), pages 43-84, March.
    3. Guhl, Daniel, 2019. "Addressing endogeneity in aggregate logit models with time-varying parameters for optimal retail-pricing," European Journal of Operational Research, Elsevier, vol. 277(2), pages 684-698.
    4. Shinya Sugawara, 2017. "Firm‐Driven Management of Longevity Risk: Analysis of Lump‐Sum Forward Payments in Japanese Nursing Homes," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 26(1), pages 169-204, February.
    5. Sun, Yutec & Ishihara, Masakazu, 2019. "A computationally efficient fixed point approach to dynamic structural demand estimation," Journal of Econometrics, Elsevier, vol. 208(2), pages 563-584.
    6. Zenetti, German & Klapper, Daniel, 2016. "Advertising Effects Under Consumer Heterogeneity – The Moderating Role of Brand Experience, Advertising Recall and Attitude," Journal of Retailing, Elsevier, vol. 92(3), pages 352-372.
    7. Ali Hortaçsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," NBER Working Papers 29508, National Bureau of Economic Research, Inc.
    8. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    9. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.
    10. 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.
    11. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.
    12. Lou, Weifang & Prentice, David & Yin, Xiangkang, 2008. "The Effects of Product Ageing on Demand: The Case of Digital Cameras," MPRA Paper 13407, University Library of Munich, Germany.
    13. Simon P. Anderson & Federico Ciliberto & Jura Liaukonyte & Régis Renault, 2016. "Push-me pull-you: comparative advertising in the OTC analgesics industry," RAND Journal of Economics, RAND Corporation, vol. 47(4), pages 1029-1056, November.
    14. 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.
    15. Qizhong Yang & Keiichiro Honda & Tsunehiro Otsuki, 2019. "Structural demand estimation of the response to food safety regulations in the Japanese poultry market," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(3), pages 367-385, September.
    16. Charles Murry & Yiyi Zhou, 2020. "Consumer Search and Automobile Dealer Colocation," Management Science, INFORMS, vol. 66(5), pages 1909-1934, May.
    17. Matzkin, Rosa L., 2012. "Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 166(1), pages 106-115.
    18. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1445-1488, December.
    19. Carranza, Juan Esteban, 2010. "Product innovation and adoption in market equilibrium: The case of digital cameras," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 604-618, November.
    20. Masakazu Ishihara & Andrew T. Ching, 2019. "Dynamic Demand for New and Used Durable Goods Without Physical Depreciation: The Case of Japanese Video Games," Marketing Science, INFORMS, vol. 38(3), pages 392-416, May.

    More about this item

    Keywords

    Bayesian estimation; random coefficient logit; aggregate share models;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    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:pra:mprapa:26449. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.