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A Comparison of Parametric Approximation Techniques to Continuous-Time Stochastic Dynamic Programming Problems

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  • Kompas, Tom
  • Chu, Long

Abstract

We compare three parametric techniques to approximate Hamilton-Jacobi-Bellman equations via unidimensional and multidimensional problems. The linear programming technique is very efficient for unidimensional problems and offers a balance of speed and accuracy for multidimensional problems. A comparable projection technique is shown to be slow, but has stable accuracy, whereas a perturbation technique has the least accuracy although its speed suffers least from the curse of dimensionality. The linear programming technique is also shown to be suitable for problems in resource management, including applications to biosecurity and marine reserve design.

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Bibliographic Info

Paper provided by Australian National University, Environmental Economics Research Hub in its series Research Reports with number 95044.

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Date of creation: Sep 2010
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Handle: RePEc:ags:eerhrr:95044

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Keywords: stochastic dynamic programming; parametric approximation; perturbation; projection; linear programming; optimal fishing; marine reserves; Research Methods/ Statistical Methods; Resource /Energy Economics and Policy; C61; C63; Q22;

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  1. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, Spring.
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Cited by:
  1. Juan Carlos Parra-Alvarez, 2013. "A comparison of numerical methods for the solution of continuous-time DSGE models," CREATES Research Papers 2013-39, School of Economics and Management, University of Aarhus.

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