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A computationally efficient fixed point approach to dynamic structural demand estimation

Yutec Sun and Masakazu Ishihara

Journal of Econometrics, 2019, vol. 208, issue 2, 563-584

Abstract: This paper develops a computationally efficient approach to the estimation of dynamic structural demand with product panel data. The conventional GMM approach relies on two nested fixed point (NFP) algorithms, each developed by Rust (1987) and Berry, Levinsohn, and Pakes (1995). We transform the GMM into a quasi-Bayesian (Laplace type) estimator and develop a new MCMC method that efficiently solves the fixed point problems. Our approach requires no stronger assumptions than the GMM and can thus avoid bias from misspecified models. In Monte Carlo analysis, the new method outperforms both NFP and MPEC, particularly in large-scale estimations.

Keywords: Nested fixed point; BLP; Dynamic; MCMC; Random coefficients logit (search for similar items in EconPapers)
JEL-codes: C11 C13 C51 (search for similar items in EconPapers)
Date: 2019
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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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Handle: RePEc:eee:econom:v:208:y:2019:i:2:p:563-584