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Revisiting the nested fixed-point algorithm in BLP random coefficients demand estimation
- Authors
- Lee, Jinhyuk; Seo, Kyoungwon
- Issue Date
- 12월-2016
- Publisher
- ELSEVIER SCIENCE SA
- Keywords
- Random coefficients logit demand; Numerical methods; Nested fixed-point algorithm; Newton' s method
- Citation
- ECONOMICS LETTERS, v.149, pp.67 - 70
- Indexed
- SSCI
SCOPUS
- Journal Title
- ECONOMICS LETTERS
- Volume
- 149
- Start Page
- 67
- End Page
- 70
- ISSN
- 0165-1765
- Abstract
- 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. Dube 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. (C) 2016 Elsevier B.V. All rights reserved.
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