Accuracy of Deterministic Extended-Path Solution Methods for Dynamic Stochastic Optimization Problems in Macroeconomics
The deterministic extended-path method for solving dynamic stochastic optimization problems approximates conditional expectations instead of approximating a model's complex non-linear dynamics. We show that this straightforward approach provides similar accuracy to the best results reported for alternative methods, and gives uniform performance across the entire state space. Our implementation requires roughly 4 fold more computer time than Galerkin projection, but the method has offsetting simplicity and generality that make it an attractive choice.
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