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Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration

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  • Peter Arcidiacono
  • Patrick Bayer
  • Federico Bugni
  • Jon James

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 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.

Suggested Citation

  • 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.
    • Handle: RePEc:duk:dukeec:12-07
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    References listed on IDEAS

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    Cited by:

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    3. Shintaro Yamaguchi, 2016. "Effects of Parental Leave Policies on Female Career and Fertility Choices," Department of Economics Working Papers 2016-10, McMaster University.

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    More about this item

    JEL classification:

    • 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

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