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Jacobian-free Efficient Pseudo-Likelihood (EPL) algorithm

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  • Fukasawa, Takeshi

Abstract

This study proposes a simple procedure for computing Efficient Pseudo Likelihood (EPL) estimator (Dearing and Blevins, 2024) for estimating dynamic discrete games, without computing Jacobians of equilibrium constraints. It utilizes the ideas of numerical derivatives and the Krylov method.

Suggested Citation

  • Fukasawa, Takeshi, 2025. "Jacobian-free Efficient Pseudo-Likelihood (EPL) algorithm," Economics Letters, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:ecolet:v:247:y:2025:i:c:s0165176525000321
    DOI: 10.1016/j.econlet.2025.112195
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    References listed on IDEAS

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    1. Mitsuru Igami, 2017. "Estimating the Innovator’s Dilemma: Structural Analysis of Creative Destruction in the Hard Disk Drive Industry, 1981–1998," Journal of Political Economy, University of Chicago Press, vol. 125(3), pages 798-847.
    2. Adam Dearing S.C. & Jason R Blevins, 2025. "Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(2), pages 981-1021.
    3. Victor Aguirregabiria & Pedro Mira, 2007. "Sequential Estimation of Dynamic Discrete Games," Econometrica, Econometric Society, vol. 75(1), pages 1-53, January.
    4. Michael Egesdal & Zhenyu Lai & Che‐Lin Su, 2015. "Estimating dynamic discrete‐choice games of incomplete information," Quantitative Economics, Econometric Society, vol. 6(3), pages 567-597, November.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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