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Lee, Jin Hyuk
School of Business Administration
Research Interests
  • Industrial Organization
  • Applied Microeconomics
  • Applied Econometrics

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A computationally fast estimator for random coefficients logit demand models using aggregate data

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Title
A computationally fast estimator for random coefficients logit demand models using aggregate data
Author
Lee, Jin HyukSeo, Kyoungwon
Issue Date
2015-03
Publisher
WILEY-BLACKWELL
Citation
RAND JOURNAL OF ECONOMICS, v.46, no.1, pp.86 - 102
Abstract
This article proposes a computationally fast estimator for random coefficients logit demand models using aggregate data that Berry, Levinsohn, and Pakes (; hereinafter, BLP) suggest. Our method, which we call approximate BLP (ABLP), is based on a linear approximation of market share functions. The computational advantages of ABLP include (i) the linear approximation enables us to adopt an analytic inversion of the market share equations instead of a numerical inversion that BLP propose, (ii) ABLP solves the market share equations only at the optimum, and (iii) it minimizes over a typically small dimensional parameter space. We show that the ABLP estimator is equivalent to the BLP estimator in large data sets. Our Monte Carlo experiments illustrate that ABLP is faster than other approaches, especially for large data sets
URI
https://scholarworks.unist.ac.kr/handle/201301/10846
URL
http://onlinelibrary.wiley.com/doi/10.1111/1756-2171.12078/abstract;jsessionid=CD12F6673E797C6D3F027065E160EEFB.f04t04
DOI
10.1111/1756-2171.12078
ISSN
0741-6261
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