General Model-based Filters for Extracting Cycles and Trends in Economic Time Series
AbstractA new class of model-based filters for extracting trends and cycles in economic time series is presented. These low pass and band pass filters are derived in a mutually consistent manner as the joint solution to a signal extraction problem in an unobserved components model. The resulting trends and cycles are computed in finite samples using a Kalman filter and associated smoother. The filters form a class which is a generalisation of the class of Butterworth filters, widely used in engineering. They are very flexible and have the important property of allowing relatively smooth cycles to be extracted from economic time series. Perfectly sharp, or ideal, band pass filters emerge as a special case. Applying the method to a quarterly series on US investment shows a clearly defined cycle currently at the peak of a boom.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0113.
Date of creation: Jul 2001
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band pass filter; Butterworth filter; ideal filter; Kalman filter; signal extraction; unobserved components;
Other versions of this item:
- Andrew C. Harvey & Thomas M. Trimbur, 2003. "General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 244-255, May.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-07-30 (All new papers)
- NEP-DGE-2001-07-30 (Dynamic General Equilibrium)
- NEP-ECM-2001-07-30 (Econometrics)
- NEP-ETS-2001-07-30 (Econometric Time Series)
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