The method of endogenous gridpoints for solving dynamic stochastic optimization problems
AbstractThis paper introduces a method for solving numerical dynamic stochastic optimization problems that avoids rootfinding operations. The idea is applicable to many microeconomic and macroeconomic problems, including life cycle, buffer-stock, and stochastic growth problems. Software is provided.
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Bibliographic InfoArticle provided by Elsevier in its journal Economics Letters.
Volume (Year): 91 (2006)
Issue (Month): 3 (June)
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Web page: http://www.elsevier.com/locate/ecolet
Other versions of this item:
- Christopher Carroll, 2005. "The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems," Economics Working Paper Archive 520, The Johns Hopkins University,Department of Economics.
- Carroll, Christopher D., 2005. "The method of endogenous gridpoints for solving dynamic stochastic optimization problems," CFS Working Paper Series 2005/18, Center for Financial Studies (CFS).
- Christopher D. Carroll, 2005. "The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems," NBER Technical Working Papers 0309, National Bureau of Economic Research, Inc.
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- D9 - Microeconomics - - Intertemporal Choice
- E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Carroll, Christopher D., 2011. "Theoretical foundations of buffer stock saving," CFS Working Paper Series 2011/15, Center for Financial Studies (CFS).
- Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, January.
- den Haan, Wouter J & Marcet, Albert, 1990.
"Solving the Stochastic Growth Model by Parameterizing Expectations,"
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