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

Jinhyuk Lee, Kyoungwon Seo

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This paper examines the numerical properties of the nested fixed-point algorithm (NFP) in the estimation of Berry et al. (1995) random coefficient logit demand model. Dubé et al. (2012) find the bound on the errors of the NFP estimates computed by contraction mappings (NFP/CTR) has the order of the square root of the inner loop tolerance. Under our assumptions, we theoretically derive an upper bound on the numerical bias in the NFP/CTR, which has the same order of the inner loop tolerance. We also discuss that, compared with NFP/CTR, NFP using Newton's method has a smaller bound on the estimate error.

Original languageEnglish
Pages (from-to)67-70
Number of pages4
JournalEconomics Letters
Publication statusPublished - 2016 Dec 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government NRF-2014S1A5A8018374 (Seo) and Korea University Grant K1520071 (Lee). We thank Kyoo il Kim, Daniel Ackerberg and in particular Jeremy Fox for very helpful discussions.

Publisher Copyright:
© 2016 Elsevier B.V.

Copyright 2016 Elsevier B.V., All rights reserved.


  • Nested fixed-point algorithm
  • Newton's method
  • Numerical methods
  • Random coefficients logit demand

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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