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Fitted value function iteration with probability one contractions

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  • Pál, Jenő
  • Stachurski, John

Abstract

This paper studies a value function iteration algorithm based on nonexpansive function approximation and Monte Carlo integration that can be applied to almost all stationary dynamic programming problems. The method can be represented using a randomized fitted Bellman operator and a corresponding algorithm that is shown to be globally convergent with probability one. When additional restrictions are imposed, an OP(n−1/2) rate of convergence for Monte Carlo error is obtained.

Suggested Citation

  • Pál, Jenő & Stachurski, John, 2013. "Fitted value function iteration with probability one contractions," Journal of Economic Dynamics and Control, Elsevier, vol. 37(1), pages 251-264.
  • Handle: RePEc:eee:dyncon:v:37:y:2013:i:1:p:251-264
    DOI: 10.1016/j.jedc.2012.08.003
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    References listed on IDEAS

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    Cited by:

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    2. Kristensen, Dennis & Mogensen, Patrick K. & Moon, Jong Myun & Schjerning, Bertel, 2021. "Solving dynamic discrete choice models using smoothing and sieve methods," Journal of Econometrics, Elsevier, vol. 223(2), pages 328-360.
    3. Robert Kirkby Author-Email: robertkirkby@gmail.com|, 2017. "Convergence of Discretized Value Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 117-153, January.

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    More about this item

    Keywords

    Dynamic programming; Value function iteration; Monte Carlo;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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