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One-node Quadrature Beats Monte Carlo: A Generalized Stochastic Simulation Algorithm

Author

Listed:
  • Kenneth Judd
  • Lilia Maliar
  • Serguei Maliar

Abstract

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.

Suggested Citation

  • Kenneth Judd & Lilia Maliar & Serguei Maliar, 2011. "One-node Quadrature Beats Monte Carlo: A Generalized Stochastic Simulation Algorithm," NBER Working Papers 16708, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:16708
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    References listed on IDEAS

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    1. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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