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

Jinhyuk Lee and Kyoungwon Seo

Economics Letters, 2016, vol. 149, issue C, 67-70

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. 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.

Keywords: Random coefficients logit demand; Numerical methods; Nested fixed-point algorithm; Newton’s method (search for similar items in EconPapers)
JEL-codes: C1 L1 (search for similar items in EconPapers)
Date: 2016
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