Zenetti, German (2010): A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach.
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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.
Item Type:  MPRA Paper 

Original Title:  A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach 
English Title:  A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach 
Language:  English 
Keywords:  Bayesian estimation, random coefficient logit, aggregate share models 
Subjects:  M  Business Administration and Business Economics; Marketing; Accounting > M3  Marketing and Advertising C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  26449 
Depositing User:  German Zenetti 
Date Deposited:  07. Nov 2010 22:49 
Last Modified:  20. Feb 2013 14:00 
References:  Albuquerque, P. & Bronnenberg, B. J. (2008), `Market areas of car dealerships'. Berry, S. T. (1994), `Estimating discrete choice models of product differentiation', RAND Journal of Economics 25(2), 242262. Berry, S. T., Levinsohn, J. & Pakes, A. (1995), `Automobile prices in market equilibrium', Econometrica 63(4), 841890. Bhat, C. R. (2000), `Quasirandom maximum simulated likelihood estimation of the mixed logit model', Transportation Research Part B(35), 677693. Dube, J., Fox, J. & Su, C. (2008), `Improving the numerical performance of blp static and dynamic discrete choice random coefficients demand estimation'. Gowrisankaran, G. & Rysman, M. (2009), Dynamics of consumer demand for new durable goods. Working Paper. Hausman, J. (1954), `Specication tests in econometrics', Econometrica 46(3), 12511271. Heckman, J. J. (1978), `Dummy endogenous variables in a simultaneous equation system', Econometrica 46(6), 931959. Heiss, F. & Winschel, V. (2006), Estimation with numerical integration on sparse grids. Discussion paper 200615. Jiang, R., Manchanda, P. & Rossi, Peter, E. (2009), `Baysian analysis of random coefficients logit models using aggregate data', Journal of Econometrics 149(2), 136148. Judd, K. L. (1998), Numerical Methods in Econometrics, MIT Press, Cambridge, Mass. Nevo, A. (2000), `A practitioner's guide to estimation of random coefficients logit models of demand', Journal of Economics & Management Strategy 9(4), 513538. Park, S. & Gupta, S. (2009), `Simulated maximumlikelihood estimator for the random coefficient logit model using aggregate data', American Marketing Association 46, 531542. Petrin, A. & Train, K. (2002), Omitted product attributes in discrete choice models, Working paper, University of Chicago. December 16. Rossi, P. E., Allenby, G. M. & McCulloch, R. (2005), Bayesian Statistics and Marketing, Wiley Series in Probability and Statistics, West Sussex. Smolyak, S. A. (1963), `Quadrature and interpolation formulas for tensor products of certain classe of functions', Soviet Mathematics Doklady 4, 240243. Sovinsky Goeree, M. (2008), `Limited information and advertising in the u.s. personal computer industry', Econometrica 76(5), 10171074. Train, K. E. (2000), Halton sequences for mixed logit, Working paper, Department of Economics, University of California, Berkley. Walker, L. J., BenAkiva, M. & Bolduc, D. (2007), `Identication of parameters in normal error component logitmixture (neclm) models', Journal of Applied Econometrics 22(6), 10951125. 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/26449 
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