Time Series Simulation With Quasi-Monte Carlo Methods
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.
|Date of creation:||05 Jul 2000|
|Date of revision:|
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- Neil R. Ericsson & Jaime R. Marquez, 1998.
"A framework for economic forecasting,"
International Finance Discussion Papers
626, Board of Governors of the Federal Reserve System (U.S.).
- Franz, Wolfgang & Göggelmann, Klaus & Schellhorn, Martin & Winker, Peter, 1998. "Quasi-Monte Carlo Methods in Stochastic Simulations: An Application to Fiscal Policy Simulations using an Aggregate Disequilibrium Model of the West German Economy," ZEW Discussion Papers 98-03, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
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