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Solving higher-dimensional continuous-time stochastic control problems by value function regression

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  • Reiter, Michael

Abstract

The paper develops a method to solve higher-dimensional stochastic control problems in continuous time. A finite difference type approximation scheme is used on a coarse grid of low discrepancy points, while the value function at intermediate points is obtained by regression. The stability properties of the method are discussed, and applications are given to test problems of up to 10 dimensions. Accurate solutions to these problems can be obtained on a personal computer.
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  • Reiter, Michael, 1999. "Solving higher-dimensional continuous-time stochastic control problems by value function regression," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1329-1353, September.
    • Handle: RePEc:eee:dyncon:v:23:y:1999:i:9-10:p:1329-1353
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    References listed on IDEAS

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    1. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-672, November.
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    3. John Rust, 1997. "A Comparison of Policy Iteration Methods for Solving Continuous-State, Infinite-Horizon Markovian Decision Problems Using Random, Quasi-random, and Deterministic Discretizations," Computational Economics 9704001, University Library of Munich, Germany.
    4. Michael Reiter, "undated". "Solving Higher-Dimensional Continuous Time Stochastic Control Problems by Value Function Interpolation," Computing in Economics and Finance 1997 135, Society for Computational Economics.
    5. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
    6. Judd, Kenneth L., 1996. "Approximation, perturbation, and projection methods in economic analysis," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 12, pages 509-585, Elsevier.
    7. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729, Elsevier.
    8. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
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    Cited by:

    1. Lars Grüne & Willi Semmler, 2007. "Asset pricing with dynamic programming," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 233-265, May.
    2. Willi Semmler & Lars Grüne, 2004. "Asset Pricing with Delayed Consumption Decisions," Computing in Economics and Finance 2004 59, Society for Computational Economics.
    3. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    4. Warren J. Hahn & James S. Dyer, 2011. "A Discrete Time Approach for Modeling Two-Factor Mean-Reverting Stochastic Processes," Decision Analysis, INFORMS, vol. 8(3), pages 220-232, September.
    5. Grune, Lars & Semmler, Willi, 2004. "Using dynamic programming with adaptive grid scheme for optimal control problems in economics," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2427-2456, December.
    6. Ewald, Christian Oliver & Nolan, Charles, 2024. "On the adaptation of the Lagrange formalism to continuous time stochastic optimal control: A Lagrange-Chow redux," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    7. Alemdar, Nedim M. & Sirakaya, Sibel & Husseinov, Farhad, 2006. "Optimal time aggregation of infinite horizon control problems," Journal of Economic Dynamics and Control, Elsevier, vol. 30(4), pages 569-593, April.

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    JEL classification:

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

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