Approximating high-dimensional dynamic models: sieve value function iteration
AbstractMany 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1210.
Date of creation: 2012
Date of revision:
Other versions of this item:
- Peter Arcidiacono & Patrick Bayer & Federico Bugni & Jon James, 2012. "Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration," Working Papers 12-07, Duke University, Department of Economics.
- Peter Arcidiacono & Patrick Bayer & Federico A. Bugni & Jonathan James, 2012. "Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration," NBER Working Papers 17890, National Bureau of Economic Research, Inc.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-15 (All new papers)
- NEP-CMP-2012-05-15 (Computational Economics)
- NEP-DGE-2012-05-15 (Dynamic General Equilibrium)
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- Victor Aguirregabiria & Pedro Mira, 1999.
"Swapping the Nested Fixed-Point Algorithm: a Class of Estimators for Discrete Markov Decision Models,"
Computing in Economics and Finance 1999
332, Society for Computational Economics.
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- Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, 07.
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