One-node Quadrature Beats Monte Carlo: A Generalized Stochastic Simulation Algorithm
AbstractIn 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.
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Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 16708.
Date of creation: Jan 2011
Date of revision:
Publication status: published as Kenneth L. Judd, Lilia Maliar and Serguei Maliar, (2011). “Numerically Stable and Accurate Stochastic Simulation Methods for Solving Dynamic Models" and "Supplement", Quantitative Economics 2, 173-210.
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- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-01-30 (All new papers)
- NEP-CMP-2011-01-30 (Computational Economics)
- NEP-ORE-2011-01-30 (Operations Research)
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