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Structural Breaks and interest rates forecast: a sequential approach

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Author Info
José Luis Fernández Serrano
Mª Dolores Robles Fernández

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Abstract

The analysis of the future behaviour of economic variables can be biased if structural breaks are not considered. When these structural breaks are present, the in-sample fit of a model gives us a poor guide to ex-ante forecast performance. This problem is shared by univariate and multivariate analysis and can be extremely important when cointegration relationships are analysed. The main goal of this paper consists in analysing the impact of structural breaks on forecast accuracy evaluation. We are concerned in forecasting several interest rates from the Spanish interbank money market. In order to carry out the analysis, we perform two forecasting exercises: (1) without structural breaks and (2) when structural breaks are explicitly considered. We use new sequential methods in order to estimate change-points in an endogenous way. After which, we compare the out-of-sample forecast ability of these models. Our results may indicate scarce gains when the structural break is included in the models. El análisis del comportamiento futuro de las variables económicas puede estar sesgado si se ignora la presencia de cambios estructurales. Cuando se dan esos cambios, el ajuste de los modelos dentro de la muestra nos proporciona poca información ex-ante sobre el comportaiento en previsión. Este problema lo comparten el análisis univariante y multivariante y puede ser muy importante en el caso de que existan relaciones de cointegración. El objetivo principal de este trabajo consiste en analizar el impacto de los cambios estructurales en la evaluación de la capacidad predictiva. Estamos interesados en la previsión de los tipos de interés del mercado interbancario. Utilizamos nuevos métodos secuenciales para estimar los puntos de corte de manera endógena. Posteriormente, comparamos las previsiones hechas con los modelos que incorporan los cambios estructurales detectados con modelos en los que no se han tenido en cuenta. Nuestros resultados parecen indicar escasas ganancias cuando se incorpora la información sobre el cambio estructural.

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Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales in its series Documentos del Instituto Complutense de Análisis Económico with number 0110.

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Date of creation: 2001
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Handle: RePEc:ucm:doicae:0110

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  1. Michael P. Clements & David F. Hendry, 1999. "On winning forecasting competitions in economics," Spanish Economic Review, Springer, vol. 1(2), pages 123-160. [Downloadable!] (restricted)
  2. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
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  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  4. Christiano, Lawrence J, 1992. "Searching for a Break in GNP," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 237-50, July.
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  5. Chu, Chia-Shang James & Stinchcombe, Maxwell & White, Halbert, 1996. "Monitoring Structural Change," Econometrica, Econometric Society, vol. 64(5), pages 1045-65, September. [Downloadable!] (restricted)
  6. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July. [Downloadable!] (restricted)
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  7. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March. [Downloadable!] (restricted)
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  1. José Luis Fernández-Serrano & M. Dolores Robles Fernández, 2004. "Política monetaria y cambios de régimen en los tipos de interés del mercado interbancario español," Investigaciones Economicas, Fundación SEPI, vol. 28(2), pages 349-376, May. [Downloadable!]
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