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Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration

Many dynamic problems in economics are characterized by large state spaces, which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the: (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the model's parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.

Keywords: Large state space, Dynamic decision problem, Sieve approximation, Value function, Value function iteration.

JEL Codes: C02, C44, C60, C63

Suggested citation: Arcidiacono, Peter, Patrick Bayer, Federico A. Bugni, and Jonathan James, 2012. “Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration,” Federal Reserve Bank of Cleveland, Working Paper no 12-10R.

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