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Linear Estimation of Aggregate Dynamic Discrete Demand for Durable Goods: Overcoming the Curse of Dimensionality

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Listed:
  • Cheng Chou

    (University of Leicester, Leicester LE1 7RH, United Kingdom)

  • Tim Derdenger

    (Carnegie Mellon University, Pittsburgh, Pennsylvania 15289)

  • Vineet Kumar

    (Yale University, New Haven, Connecticut 06511)

Abstract

We develop a new approach using market-level data to model, identify, and estimate a dynamic discrete choice demand model for durable goods with continuous unobserved product-specific state variables. They are specified as serially correlated and correlated with the observed product characteristics, particularly price. We provide a method to estimate all model primitives, including the consumer’s discount factor and the state transition distributions of unobserved product characteristics without the need to reduce the dimension of the state space or by other approximation techniques, such as discretizing state variables. We prove the identification of model primitives and provide an estimation algorithm in which the most computationally demanding step is a linear regression. Finally, we show how it can be implemented in an application in which we estimate the demand for smartphones.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormksc:v:38:y:2019:i:5:p:888-909
    DOI: 10.1287/mksc.2019.1182
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    References listed on IDEAS

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    Cited by:

    1. Timothy Derdenger & Vineet Kumar, 2019. "Estimating dynamic discrete choice models with aggregate data: Properties of the inclusive value approximation," Quantitative Marketing and Economics (QME), Springer, vol. 17(4), pages 359-384, December.
    2. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    3. Doug J. Chung & Byungyeon Kim & Byoung G. Park, 2021. "The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training," Management Science, INFORMS, vol. 67(11), pages 7046-7074, November.
    4. Cheng Chou & Geert Ridder & Ruoyao Shi, 2024. "Identification and Estimation of Nonstationary Dynamic Binary Choice Models," Working Papers 202402, University of California at Riverside, Department of Economics.
    5. Yue Liu & Rong Luo, 2023. "Network Effects and Multinetwork Sellers’ Dynamic Pricing in the U.S. Smartphone Market," Management Science, INFORMS, vol. 69(6), pages 3297-3318, June.
    6. 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.

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