Testing the nested fixed-point algorithm in BLP random coefficients demand estimation

Jinhyuk Lee, Kyoungwon Seo

Research output: Contribution to journalArticle


This paper examines the numerical properties of the nested fixed point algorithm (NFP) using Monte Carlo experiments in the estimation of Berry, Levinsohn, and Pakes’s (1995) random coefficient logit demand model. We find that in speed, convergence and accuracy, nested fixed-point (NFP) approach using Newton’s method performs well like a mathematical programming with equilibrium constraints (MPEC) approach adopted by Dubé, Fox, and Su (2012).

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalJournal of Economic Theory and Econometrics
Issue number4
Publication statusPublished - 2017 Dec 1



  • Nested fixedpoint algorithm
  • Newton’s method
  • Numerical methods
  • Random coefficients logit demand

ASJC Scopus subject areas

  • Economics and Econometrics

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