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

Jinhyuk Lee, Kyoungwon Seo

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)1-21
Number of pages21
JournalJournal of Economic Theory and Econometrics
Volume28
Issue number4
Publication statusPublished - 2017 Dec 1

Fingerprint

Demand estimation
Random coefficients
Fixed point
Testing
Demand model
Monte Carlo experiment
Logit
Mathematical programming
Convergence speed

Keywords

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

ASJC Scopus subject areas

  • Economics and Econometrics

Cite this

Testing the nested fixed-point algorithm in BLP random coefficients demand estimation. / Lee, Jinhyuk; Seo, Kyoungwon.

In: Journal of Economic Theory and Econometrics, Vol. 28, No. 4, 01.12.2017, p. 1-21.

Research output: Contribution to journalArticle

@article{7e96b57454154eaf996e8a3975b6c779,
title = "Testing the nested fixed-point algorithm in BLP random coefficients demand estimation",
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 Dub{\'e}, Fox, and Su (2012).",
keywords = "Nested fixedpoint algorithm, Newton’s method, Numerical methods, Random coefficients logit demand",
author = "Jinhyuk Lee and Kyoungwon Seo",
year = "2017",
month = "12",
day = "1",
language = "English",
volume = "28",
pages = "1--21",
journal = "Journal of Economic Theory and Econometrics",
issn = "1229-2893",
publisher = "Korean Econometric Society",
number = "4",

}

TY - JOUR

T1 - Testing the nested fixed-point algorithm in BLP random coefficients demand estimation

AU - Lee, Jinhyuk

AU - Seo, Kyoungwon

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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).

AB - 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).

KW - Nested fixedpoint algorithm

KW - Newton’s method

KW - Numerical methods

KW - Random coefficients logit demand

UR - http://www.scopus.com/inward/record.url?scp=85039857942&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85039857942&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85039857942

VL - 28

SP - 1

EP - 21

JO - Journal of Economic Theory and Econometrics

JF - Journal of Economic Theory and Econometrics

SN - 1229-2893

IS - 4

ER -