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• W1553316614 abstract "A widely used filter to extract a signal in a time series, in particular in the business cycle analysis, is the Hodrick-Prescott filter. The model that underlies the filter considers the data series as the sum of two unobserved component (signal and non signal) and a smoothing parameter which for quarterly series is set to a specified value. This paper proposes a generalization of the Hodrick-Prescott filter to a continuous time support, using the well-established relationship between cubic splines and state-space models. The spline formulation of the filter leads to a state space model with several practical advantages: first, the smoothing parameter can be either pre-specified or estimated as the other parameters in the model; second, the unobserved components can be modelled by the addition of particular ARIMA structures; lastly the model is capable of working in the presence of missing values or for irregular surveys. Monte Carlo experiments support these considerations." @default.
• W1553316614 created "2016-06-24" @default.
• W1553316614 creator A5047313801 @default.
• W1553316614 creator A5091862570 @default.
• W1553316614 date "2003-11-07" @default.
• W1553316614 modified "2023-10-08" @default.
• W1553316614 title "Signal Extraction in Continuous Time and the Generalized Hodrick- Prescott Filter" @default.
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