Abstract
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 language | English |
|---|---|
| Pages (from-to) | 1-21 |
| Number of pages | 21 |
| Journal | Journal of Economic Theory and Econometrics |
| Volume | 28 |
| Issue number | 4 |
| Publication status | Published - 2017 Dec 1 |
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Keywords
- Nested fixedpoint algorithm
- Newton’s method
- Numerical methods
- Random coefficients logit demand
ASJC Scopus subject areas
- Economics and Econometrics
Cite this
Lee, J., & Seo, K. (2017). Testing the nested fixed-point algorithm in BLP random coefficients demand estimation. Journal of Economic Theory and Econometrics, 28(4), 1-21.