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Continuous State Dynamic Programming Via Nonexpansive Approximation

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  • John Stachurski

    (Department of Economics, University of Melbourne)

Abstract

This paper studies fitted value iteration for continuous state dynamic programming using nonexpansive function approximators. A number of nonexpansive approximation schemes are discussed. The main contribution is to provide error bounds for approximate optimal policies generated by the value iteration algorithm.

Suggested Citation

  • John Stachurski, 2006. "Continuous State Dynamic Programming Via Nonexpansive Approximation," KIER Working Papers 618, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:618
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    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP618.pdf
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    References listed on IDEAS

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    Cited by:

    1. Arellano, Cristina & Maliar, Lilia & Maliar, Serguei & Tsyrennikov, Viktor, 2016. "Envelope condition method with an application to default risk models," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 436-459.
    2. Fukushima, Kenichi & Waki, Yuichiro, 2013. "A polyhedral approximation approach to concave numerical dynamic programming," Journal of Economic Dynamics and Control, Elsevier, vol. 37(11), pages 2322-2335.
    3. Huiyu Li, 2015. "Leverage and Productivity," Discussion Papers 15-015, Stanford Institute for Economic Policy Research.
    4. Takefumi Yamazaki, 2018. "Accuracy and speed of the solution methods for sovereign default models: The stable performance of the Tauchen method and cubic spline interpolation," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 14(4), pages 641-662, July.
    5. Jenö Pál & John Stachurski, 2011. "Fitted Value Function Iteration With Probability One Contractions," ANU Working Papers in Economics and Econometrics 2011-560, Australian National University, College of Business and Economics, School of Economics.
    6. Doupe, Patrick, 2014. "Reduced deforestation and economic growth," Working Papers 249421, Australian National University, Centre for Climate Economics & Policy.
    7. Nishimura, Kazuo & Stachurski, John, 2010. "Perfect simulation of stationary equilibria," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 577-584, April.
    8. John Stachurski, 2009. "Economic Dynamics: Theory and Computation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012774, December.
    9. Takefumi Yamazaki, 2018. "Financial friction sources in emerging economies: Structural estimation of sovereign default models," Discussion papers ron303, Policy Research Institute, Ministry of Finance Japan.
    10. Richard Anton Braun & Huiyu Li & John Stachurski, 2009. "Computing Densities and Expectations in Stochastic Recursive Economies: Generalized Look-Ahead Techniques," CIRJE F-Series CIRJE-F-620, CIRJE, Faculty of Economics, University of Tokyo.
    11. Patrick Doupe, 2014. "Reduced Deforestation and Economic Growth," CCEP Working Papers 1402, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
    12. 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.
    13. Huiyu Li, 2015. "Numerical Policy Error Bounds for $$\eta $$ η -Concave Stochastic Dynamic Programming with Non-interior Solutions," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 171-187, August.
    14. 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.

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    More about this item

    JEL classification:

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

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