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Quasi-Monte Carlo methods in stochastic simulations: An application to policy simulations using a disequilibrium model of the West German economy 1960-1994

Author

Listed:
  • Klaus Göggelmann

    (Credit Suisse Asset Management, Uetlibergstrasse 231, CH-8070 Zürich, Switzerland)

  • Peter Winker

    (Fakultät für Volkswirtschaftlehre, Universität Mannheim, A5, D-68131 Mannheim, Germany)

  • Martin Schellhorn

    (Volkswirtschaftliches Institut, Universität Bern, Gesellschaftsstr. 49, CH-3012 Bern, Switzerland)

  • Wolfgang Franz

    (Zentrum für Europäische Wirtschaftsforschung, P.O. Box 10 34 43, D-68034 Mannheim, Germany)

Abstract

Different stochastic simulation methods are used in order to check the robustness of the outcome of policy simulations. The application of a macroeconometric disequilibrium model of the West German economy to a fiscal policy simulation is taken as an example. Due to nonlinearities arising from regime specific reactions inside the model, confidence intervals for the simulation results have to be obtained by means of stochastic simulations. The robustness of the results is assessed using different methodologies. In particular, different methods for the generation of uniform error terms and their conversion to normal variates are applied. These methods include standard approaches as well as quasi-Monte Carlo methods.

Suggested Citation

  • Klaus Göggelmann & Peter Winker & Martin Schellhorn & Wolfgang Franz, 2000. "Quasi-Monte Carlo methods in stochastic simulations: An application to policy simulations using a disequilibrium model of the West German economy 1960-1994," Empirical Economics, Springer, vol. 25(2), pages 247-259.
    • Handle: RePEc:spr:empeco:v:25:y:2000:i:2:p:247-259
    Note: received: May 1998/final version accepted: August 1999
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    Citations

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    Cited by:

    1. Mihoko V. Bennett & Thomas R. Willemain, 2004. "The Filtered Nearest Neighbor Method for Generating Low-Discrepancy Sequences," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 68-72, February.
    2. Dag Kolsrud, 2015. "A Time‐Simultaneous Prediction Box for a Multivariate Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(8), pages 675-693, December.
    3. Dag Kolsrud, 2008. "Stochastic Ceteris Paribus Simulations," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 21-43, February.
    4. Yu-Ying Tzeng & Paul M. Beaumont & Giray Ökten, 2018. "Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 55-77, June.

    More about this item

    Keywords

    Policy simulation; stochastic simulation; random number generation; quasi-Monte Carlo methods;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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