Estimation and accuracy of the stationary solution in the dynamic programming problem: New results
نویسنده
چکیده
In this paper we give explicit error bounds for approximations of the optimal policy function in the stochastic dynamic programming problem. The approximated policy function is obtained by using the Bellman equation with an approximated value function and the error bounds depend on the primitive data of the problem. Neither differentiability of the return function nor interiority of solutions is required. Furthermore, similar error bounds are obtained when the maximization in the Bellman equation and the computation of the associated policy function are performed inexactly. This shows the robustness of the method and provides a stopping criterium for computational implementations.
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