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A computationally efficient fixed point approach to dynamic structural demand estimation

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  • Sun, Yutec
  • Ishihara, Masakazu
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.

Suggested Citation

  • Sun, Yutec & Ishihara, Masakazu, 2019. "A computationally efficient fixed point approach to dynamic structural demand estimation," Journal of Econometrics, Elsevier, vol. 208(2), pages 563-584.
    • Handle: RePEc:eee:econom:v:208:y:2019:i:2:p:563-584
    DOI: 10.1016/j.jeconom.2018.09.021
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    Cited by:

    1. Cheng Chou & Tim Derdenger & Vineet Kumar, 2019. "Linear Estimation of Aggregate Dynamic Discrete Demand for Durable Goods: Overcoming the Curse of Dimensionality," Marketing Science, INFORMS, vol. 38(5), pages 888-909, September.
    2. Takeshi Fukasawa, 2024. "Fast and simple inner-loop algorithms of static / dynamic BLP estimations," Papers 2404.04494, arXiv.org, revised Oct 2024.
    3. Takeshi Fukasawa, 2022. "The Biases in Applying Static Demand Models under Dynamic Demand," Discussion Paper Series DP2022-18, Research Institute for Economics & Business Administration, Kobe University, revised Jul 2022.
    4. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).

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    More about this item

    Keywords

    Nested fixed point; BLP; Dynamic; MCMC; Random coefficients logit;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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