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SpMV approaches to dynamic discrete choice models with limited transition

Author

Listed:
  • Yu Wang

    (Torotno Metropolitan University)

  • Yao Luo

    (University of Toronto)

Abstract

Dynamic optimization problems often involve continuous state variables. Casting such problems into dynamic discrete choice models usually requires variable discretization. When there are multiple state variables, many discretized future states will be visited with only very small probability conditional on current states. We investigate pruning these small transition probabilities and applying the sparse matrix-vector multiplication method in value function iterations. We assess our method in a numerical example inspired by Rust (1987) and Barwick and Pathak (2015). Our method substantially improves computational performance and reduces memory requirements with little loss in accuracy.

Suggested Citation

  • Yu Wang & Yao Luo, 2022. "SpMV approaches to dynamic discrete choice models with limited transition," Economics Bulletin, AccessEcon, vol. 42(4), pages 2171-2183.
    • Handle: RePEc:ebl:ecbull:eb-22-00646
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    File URL: http://www.accessecon.com/Pubs/EB/2022/Volume42/EB-22-V42-I4-P179.pdf
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    References listed on IDEAS

    as
    1. Peter Arcidiacono & Patrick Bayer & Jason R. Blevins & Paul B. Ellickson, 2016. "Estimation of Dynamic Discrete Choice Models in Continuous Time with an Application to Retail Competition," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(3), pages 889-931.
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    More about this item

    Keywords

    Sparse; Discretization;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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