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.
Thanks to Pedro Mira, R. Vijay Krishna, and Peng Sun for useful comments and suggestions. Arcidiacono, Bayer, and Bugni thank the National Science Foundation for research support via grant SES-1124193.
Arcidiacono, P., Bayer, P., Bugni, F.A. and James, J. (2013), "Approximating High-dimensional Dynamic Models: Sieve Value Function Iteration", Structural Econometric Models (Advances in Econometrics, Vol. 31), Emerald Group Publishing Limited, Bingley, pp. 45-95. https://doi.org/10.1108/S0731-9053(2013)0000032002
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