A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach
AbstractIn 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 26449.
Date of creation: 05 Nov 2010
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Bayesian estimation; random coefficient logit; aggregate share models;
Find related papers by JEL classification:
- M3 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
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