Solving higher-dimensional continuous time stochastic control problems by value function regression
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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- Rust, J., 1994.
"Using Randomization to Break the Curse of Dimensionality,"
9429, Wisconsin Madison - Social Systems.
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- John Rust & Department of Economics & University of Wisconsin, 1994. "Using Randomization to Break the Curse of Dimensionality," Computational Economics 9403001, EconWPA, revised 04 Jul 1994.
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181, Federal Reserve Bank of Minneapolis.
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- 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.
- Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
- 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.
- 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, EconWPA.
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