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Parameter Instability and Forecasting Performance. A Monte Carlo Study

Author

Listed:
  • Anyfantakis, Costas

    (University of Piraeus)

  • Caporale, Guglielmo M.

    (London South Bank University)

  • Pittis, Nikitas

    (University of Piraeus)

Abstract

This paper uses Monte Carlo techniques to assess the loss in terms of forecast accuracy which is incurred when the true DGP exhibits parameter instability which is either overlooked or incorrectly modelled. We find that the loss is considerable when a FCM is estimated instead of the true TVCM, this loss being an increasing function of the degree of persistence and of the variance of the process driving the slope coefficient. A loss is also incurred when a TVCM different from the correct one is specified, the resulting forecasts being even less accurate than those of a FCM. However, the loss can be minimised by selecting a TVCM which, although incorrect, nests the true one, more specifically an AR(1) model with a constant. Finally, there is hardly any loss resulting from using a TVCM when the underlying DGP is characterised by fixed coefficients.

Suggested Citation

  • Anyfantakis, Costas & Caporale, Guglielmo M. & Pittis, Nikitas, 2004. "Parameter Instability and Forecasting Performance. A Monte Carlo Study," Economics Series 160, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:160
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    File URL: https://irihs.ihs.ac.at/id/eprint/1580
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    References listed on IDEAS

    as
    1. Caporale, Guglielmo Maria & Pittis, Nikitas, 2002. "Unit Roots versus Other Types of Time Heterogeneity, Parameter Time Dependence and Superexogeneity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(3), pages 207-223, April.
    2. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    3. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, January.
    4. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    5. Schinasi, Garry J. & Swamy, P. A. V. B., 1989. "The out-of-sample forecasting performance of exchange rate models when coefficients are allowed to change," Journal of International Money and Finance, Elsevier, vol. 8(3), pages 375-390, September.
    6. Cooley, Thomas F & Prescott, Edward C, 1976. "Estimation in the Presence of Stochastic Parameter Variation," Econometrica, Econometric Society, vol. 44(1), pages 167-184, January.
    7. Moryson, Martin, 1994. "Testing for Random Walk Coefficients in a Simple State Space Model," SFB 373 Discussion Papers 1994,21, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    8. Guglielmo Caporale & Nikitas Pittis, 2001. "Parameter instability, superexogeneity, and the monetary model of the exchange rate," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 137(3), pages 501-524, September.
    9. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, December.
    10. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
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    More about this item

    Keywords

    Fixed coefficient model; Time varying parameter models; Forecasting;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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