A Computationally Efficient Fixed Point Approach to Dynamic Structural Demand Estimation

41 Pages Posted: 4 Sep 2012 Last revised: 26 Jun 2018

See all articles by Yutec Sun

Yutec Sun

CREST-ENSAI - Campus de Ker-Lann

Masakazu Ishihara

New York University (NYU) - Leonard N. Stern School of Business

Date Written: April 30, 2017

Abstract

This paper develops a computationally efficient approach to the estimation of random coefficients logit model of dynamic consumer demand using product panel data. The conventional GMM estimation relies on two computationally intensive fixed point algorithms, each developed by Rust (1987) and Berry, Levinsohn, and Pakes (1995), nested within an optimization routine. We transform the GMM estimation into a quasi-Bayesian (Laplace type) framework and develop a Markov Chain Monte Carlo (MCMC) method that solves the fixed point problems incrementally with Markov chain simulation. The proposed approach has two main advantages. First, it reduces the computational burden of the nested fixed point (NFP) algorithm employed by GMM without sacrificing the model flexibility. Our Monte Carlo experiments demonstrate that the new method outperforms both NFP and MPEC in computational speed by substantial margin, particularly in the most computationally intensive estimations. Second, the proposed method requires only moment restrictions as GMM, thereby avoiding the risk of misspecification bias in equilibrium frameworks.

Keywords: Nested fixed point, BLP, dynamic programming, MCMC, Laplace, random coefficients logit

JEL Classification: C11, C13, C51

Suggested Citation

Sun, Yutec and Ishihara, Masakazu, A Computationally Efficient Fixed Point Approach to Dynamic Structural Demand Estimation (April 30, 2017). Available at SSRN: https://ssrn.com/abstract=2140617 or http://dx.doi.org/10.2139/ssrn.2140617

Yutec Sun (Contact Author)

CREST-ENSAI - Campus de Ker-Lann ( email )

Rue Blaise Pascal - BP 37203
35712 BRUZ
Rennes
France

Masakazu Ishihara

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

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