Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration
Federico Bugni and
No 12-07, Working Papers from Duke University, Department of Economics
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.
New Economics Papers: this item is included in nep-dge
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://ssrn.com/abstract=2014408 main text
Working Paper: Approximating high-dimensional dynamic models: sieve value function iteration (2012)
Working Paper: Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:12-07
Access Statistics for this paper
More papers in Working Papers from Duke University, Department of Economics Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097.
Bibliographic data for series maintained by Department of Economics Webmaster ().