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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- Michael P. Keane & Kenneth I. Wolpin, 1994.
"The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence,"
181, Federal Reserve Bank of Minneapolis.
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9403001, EconWPA, revised 04 Jul 1994.
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