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| Titel |
Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes |
| VerfasserIn |
W. Wang, P. H. A. J. M. Gelder, J. K. Vrijling, J. Ma |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 12, no. 1 ; Nr. 12, no. 1 (2005-01-21), S.55-66 |
| Datensatznummer |
250010379
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| Publikation (Nr.) |
 copernicus.org/npg-12-55-2005.pdf |
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| Zusammenfassung |
| Conventional streamflow models operate under the assumption of constant
variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized
streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However,
with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences
are found for the existence of autoregressive conditional heteroskedasticity
(i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly
streamflow processes of the upper Yellow River, China. It is shown that the
major cause of the ARCH effect is the seasonal variation in variance of the
residual series. However, while the seasonal variation in variance can fully
explain the ARCH effect for monthly streamflow, it is only a partial explanation
for daily flow. It is also shown that while the periodic autoregressive moving
average model is adequate in modelling monthly flows, no model is adequate
in modelling daily streamflow processes because none of the conventional time
series models takes the seasonal variation in variance, as well as the ARCH
effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model
is proposed to capture the ARCH effect present in daily streamflow series, as
well as to preserve seasonal variation in variance in the residuals. The
ARMA-GARCH error model combines an ARMA model for modelling the mean
behaviour and a GARCH model for modelling the variance behaviour of the
residuals from the ARMA model. Since the GARCH model is not followed widely
in statistical hydrology, the work can be a useful addition in terms of
statistical modelling of daily streamflow processes for the hydrological
community. |
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