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• W2791376391 abstract "The article explores and illustrates some of the typical trade-offs which arise in designing filters for the measurement of trends and cycles in economic time series, focusing, in particular, on the fundamental trade-off between the reliability of the estimates and the magnitude of the revisions as new observations become available. This assessment is available through a novel model based approach, according to which an important class of highpass and bandpass filters, encompassing the Hodrick–Prescott (HP) filter, are adapted to the particular time series under investigation. Via a suitable decomposition of the innovation process, it is shown that any linear time series with ARIMA representation can be broken down into orthogonal trend and cycle components, for which the class of filters is optimal. The main results then follow from Wiener–Kolmogorov (WK) signal extraction theory, whereas exact finite sample inferences are provided by the Kalman filter and smoother for the relevant state space representation of the decomposition." @default.
• W2791376391 created "2018-03-29" @default.
• W2791376391 creator A5041462602 @default.
• W2791376391 date "2008-11-18" @default.
• W2791376391 modified "2023-09-28" @default.
• W2791376391 title "On the Model-Based Interpretation of Filters and the Reliability of Trend–Cycle Estimates" @default.
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• W2791376391 doi "https://doi.org/10.1080/07474930802388025" @default.
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