Accuracy estimates for a numerical approach to stochastic growth models
In this paper we develop a discretized version of the dynamic programming algorithm and derive error bounds for the approximate value and policy functions. We show that under the proposed scheme the computed value function converges quadratically to the true value function and the computed policy function converges linearly, as the mesh size of the discretization converges to zero. Moreover, the constants involved in these orders of convergence can be computed in terms of primitive data of the model. We also discuss several aspects of the implementation of our methods, and present numerical results for some commonly studied macroeconomic models.
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