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   <dc:title>Solving higher-dimensional continuous time stochastic control problems by value function regression</dc:title>
   <dc:creator>Reiter, Michael</dc:creator>
   <dc:subject>dynamic programming</dc:subject>
   <dc:subject>stochastic control</dc:subject>
   <dc:subject>approximation</dc:subject>
   <dc:subject>Microeconomics</dc:subject>
   <dcterms:abstract>The paper develops a method to solve higher-dimensional stochastic
control problems in continuous time. A finite difference type
approximation scheme is used on a coarse grid of low discrepancy
points, while the value function at intermediate points is obtained
by regression. The stability properties of the method are discussed,
and applications are given to  test problems of up to 10 dimensions.
Accurate solutions to these problems can be obtained  on a personal
computer.</dcterms:abstract>
   <dcterms:issued>2018-02-14T15:30:00Z</dcterms:issued>
   <dcterms:issued>2018-02-14T15:30:00Z</dcterms:issued>
   <dcterms:issued>1997-03-01</dcterms:issued>
   <dcterms:issued>2017-07-23T02:03:44Z</dcterms:issued>
   <dc:type>info:eu-repo/semantics/workingPaper</dc:type>
   <dc:relation>Economics and Business Working Papers Series; 299</dc:relation>
   <dc:rights>L&amp;apos;accés als continguts d&amp;apos;aquest document queda condicionat a l&amp;apos;acceptació de les condicions d&amp;apos;ús establertes per la següent llicència Creative Commons</dc:rights>
   <dc:rights>http://creativecommons.org/licenses/by-nc-nd/3.0/es/</dc:rights>
   <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
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