Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/152933
Authors: 
Mohr, Matthias
Year of Publication: 
2005
Series/Report no.: 
ECB Working Paper 499
Abstract: 
This paper proposes a new univariate method to decompose a time series into a trend, a cyclical and a seasonal component: the Trend-Cycle filter (TC filter) and its extension, the Trend-Cycle-Season filter (TCS filter). They can be regarded as extensions of the Hodrick-Prescott filter (HP filter). In particular, the stochastic model of the HP filter is extended by explicit models for the cyclical and the seasonal component. The introduction of a stochastic cycle improves the filter in three respects: first, trend and cyclical components are more consistent with the underlying theoretical model of the filter. Second, the end-of sample reliability of the trend estimates and the cyclical component is improved compared to the HP filter since the pro-cyclical bias in end-of-sample trend estimates is virtually removed. Finally, structural breaks in the original time series can be easily accounted for.
Subjects: 
economic cycles
filtering
seasonality
time series
trend-cycle decomposition
JEL: 
C13
C22
E32
Document Type: 
Working Paper

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