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"In conventional stochastic simulation algorithms, Monte Carlo integration and curve fitting are merged together and implemented by means of regression. We perform a decomposition of the solution error and show that regression does a good job in curve fitting but a poor job in integration, which leads to low accuracy of solutions. We propose a generalized notion of stochastic simulation approach in which integration and curve fitting are separated. We specifically allow for the use of deterministic (quadrature and monomial) integration methods which are more accurate than the conventional Monte Carlo method. We achieve accuracy of solutions that is orders of magnitude higher than that of the conventional stochastic simulation algorithms"--National Bureau of Economic Research web site.
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One-node quadrature beats monte carlo: a generalized stochastic simulation algorithm
2011, National Bureau of Economic Research
Electronic resource
in English
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Book Details
Edition Notes
Title from PDF file as viewed on 4/21/2011.
Includes bibliographical references.
Also available in print.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.
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- Created July 26, 2011
- 3 revisions
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October 17, 2020 | Edited by MARC Bot | import existing book |
August 4, 2012 | Edited by VacuumBot | Updated format '[electronic resource] :' to 'Electronic resource' |
July 26, 2011 | Created by LC Bot | Imported from Library of Congress MARC record |