Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective
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Author(s)
Metaxoglou, Konstantinos
Knittel, Christopher Roland
Date Issued
March 2014
Journal
Review of Economics and Statistics
Publisher
MIT Press
Citation
Knittel, Christopher R., and Konstantinos Metaxoglou. “Estimation of Random-Coefficient Demand Models: Two Empiricists’ Perspective.” Review of Economics and Statistics 96, no. 1 (March 2014): 34–59. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology
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Final published version
Abstract
We document the numerical challenges we experienced estimating random-coefficient demand models as in Berry, Levinsohn, and Pakes (1995) using two well-known data sets and a thorough optimization design. The optimization algorithms often converge at points where the first- and second-order optimality conditions fail. There are also cases of convergence at local optima. On convergence, the variation in the values of the parameter estimates translates into variation in the models' economic predictions. Price elasticities and changes in consumer and producer welfare following hypothetical merger exercises vary at least by a factor of 2 and up to a factor of 5.
MIT Department
Sloan School of Management
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1162/REST_a_00394
