Approximating highdimensional dynamic models: Sieve value function iteration

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

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Scopus citations


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 highdimensional 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.

Original languageEnglish (US)
Title of host publicationStructural Econometric Models
PublisherJAI Press
Number of pages51
ISBN (Print)9781783500529
StatePublished - 2013
Externally publishedYes

Publication series

NameAdvances in Econometrics
ISSN (Print)0731-9053


  • Dynamic decision problem
  • Large state space
  • Sieve approximation
  • Value function
  • Value function iteration

ASJC Scopus subject areas

  • Economics and Econometrics


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