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Time Series Simulation With Quasi-Monte Carlo Methods

  • Peter Winker

    (University of Manheim)

  • Jenny Li

    (The Pennsylvania State University)

The purpose of this paper is to compare the use of quasi-Monte Carlo methods, in particular the so--called $(t,m,s)-nets$ technique, versus classical Monte Carlo approaches for the simulation of econometric time series models. Some theoretic results indicate the superiority of quasi-Monte Carlo methods. Successful applications already exist in image processing, physics, and the evaluation of finance derivatives. However, so far, quasi--Monte Carlo methods are rarely used in the field of econometrics. In this paper, we apply both traditional Monte Carlo and quasi--Monte Carlo simulation methods to time series models as they typically arise in macroeconometrics. The numerical evidence demonstrates that quasi--Monte Carlo methods outperform the traditional Monte Carlo for many time series models including non-linear and multivariate models.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 151.

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Date of creation: 05 Jul 2000
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Handle: RePEc:sce:scecf0:151
Contact details of provider: Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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  1. Franz, Wolfgang & Göggelmann, Klaus & Schellhorn, Martin & Winker, Peter, 1998. "Quasi - Monte Carlo Methods in Stochastic Simulations," ZEW Discussion Papers 98-03, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  2. Neil R. Ericsson & Jaime Marquez, 1998. "A framework for economic forecasting," International Finance Discussion Papers 626, Board of Governors of the Federal Reserve System (U.S.).
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