Approximating High-dimensional Dynamic Models: Sieve Value Function Iteration

Peter Arcidiacono, Patrick Bayer, Federico A. Bugni and Jonathan James

A chapter in Structural Econometric Models, 2013, vol. 31, pp 45-95 from Emerald Group Publishing Limited

Abstract: 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 modelik’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; C02; C44; C60; C63 (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2013)0000032002

DOI: 10.1108/S0731-9053(2013)0000032002

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