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Quasi-Monte Carlo Methods in Stochastic Simulations: An Application to Fiscal Policy Simulations using an Aggregate Disequilibrium Model of the West German Economy

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  • Franz, Wolfgang
  • Göggelmann, Klaus
  • Schellhorn, Martin
  • Winker, Peter

Abstract

Different stochastic simulation methods are used in order to check the robustness of the outcome of policy simulations with a macroeconometric model. A macroeconometric disequilibriummodel of the West German economy is used to analyze a reform proposal for the tax system. The model was estimated with quarterly data for the period 1960 to 1994, the presently possible margin. Because of nonlinearities confidence intervals for the simulation results have to be obtained by means of stochastic simulations. The main contribution of this paper consists in presenting the simulation results. The robustness of these results is analyzed using different approaches to stochastic simulation. 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

  • 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 - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:5178
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    2. J. D. Beasley & S. G. Springer, 1977. "The Percentage Points of the Normal Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(1), pages 118-121, March.
    3. Sterbenz, Frederic P & Calzolari, Giorgio, 1990. "Alternative Specifications of the Error Process in the Stochastic Simulation of Econometric Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(2), pages 137-150, April-Jun.
    4. Gilli, Manfred & Pauletto, Giorgio, 1997. "Sparse direct methods for model simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(6), pages 1093-1111, June.
    5. Brown, Bryan W & Mariano, Roberto S, 1984. "Residual-Based Procedures for Prediction and Estimation in a Nonlinear Simultaneous System," Econometrica, Econometric Society, vol. 52(2), pages 321-343, March.
    6. Schellhorn, Martin & Winker, Peter, 1994. "Stochastic simulations of a macroeconomic disequilibrium model for West Germany," Discussion Papers, Series II 235, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
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    Cited by:

    1. Dobrescu, Emilian & Pauna, Bianca, 2007. "Stochastic simulations on the Romanian macroeconomic model," MPRA Paper 35723, University Library of Munich, Germany.
    2. Jenny Li & Peter Winker, 2003. "Time Series Simulation with Quasi Monte Carlo Methods," Computational Economics, Springer;Society for Computational Economics, vol. 21(1), pages 23-43, February.
    3. Okten, Giray & Eastman, Warren, 2004. "Randomized quasi-Monte Carlo methods in pricing securities," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2399-2426, December.
    4. Beck, Martin & Winker, Peter, 2004. "Modeling spillovers and feedback of international trade in a disequilibrium framework," Economic Modelling, Elsevier, vol. 21(3), pages 445-470, May.

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