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Parameter Instability and Forecasting Performance. A Monte Carlo Study

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Author Info

  • 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.

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File URL: http://www.ihs.ac.at/publications/eco/es-160.pdf
File Function: First version, 2004
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Bibliographic Info

Paper provided by Institute for Advanced Studies in its series Economics Series with number 160.

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Length: 16 pages
Date of creation: Jul 2004
Date of revision:
Handle: RePEc:ihs:ihsesp:160

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Related research

Keywords: Fixed coefficient model; Time varying parameter models; Forecasting;

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  1. 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.
  2. Garry J. Schinasi & P.A.V.B. Swamy, 1987. "The out-of-sample forecasting performance of exchange rate models when coefficients are allowed to change," International Finance Discussion Papers 301, Board of Governors of the Federal Reserve System (U.S.).
  3. 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.
  4. Cooley, Thomas F & Prescott, Edward C, 1976. "Estimation in the Presence of Stochastic Parameter Variation," Econometrica, Econometric Society, vol. 44(1), pages 167-84, January.
  5. Martin MORYSON, 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.
  6. 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 207-23, April.
  7. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
  8. Guglielmo Caporale & Nikitas Pittis, 2001. "Parameter instability, superexogeneity, and the monetary model of the exchange rate," Review of World Economics (Weltwirtschaftliches Archiv), Springer, vol. 137(3), pages 501-524, September.
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