TY - JOUR
T1 - A computationally fast estimator for random coefficients logit demand models using aggregate data
AU - Lee, Jinhyuk
AU - Seo, Kyoungwon
PY - 2015/3/1
Y1 - 2015/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84922515501&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84922515501&partnerID=8YFLogxK
U2 - 10.1111/1756-2171.12078
DO - 10.1111/1756-2171.12078
M3 - Article
AN - SCOPUS:84922515501
VL - 46
SP - 86
EP - 102
JO - RAND Journal of Economics
JF - RAND Journal of Economics
SN - 0741-6261
IS - 1
ER -