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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    7. Heiss, Florian & Winschel, Viktor, 2006. "Estimation with Numerical Integration on Sparse Grids," Discussion Papers in Economics 916, University of Munich, Department of Economics.
    8. Kenneth Train ., 2000. "Halton Sequences for Mixed Logit," Economics Working Papers E00-278, University of California at Berkeley.
    9. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
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    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

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