Signal Extraction and the Formulation of Unobserved Components Models
AbstractThis paper looks at unobserved components models and examines the implied weighting pat- terns for signal extraction. There are three main themes. The first is the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is how setting up models with t- distributed disturbances leads to weighting patterns which are robust to outliers and breaks. The third is a comparison of implied weighting patterns with kernels used in nonparametric trend estimation and equivalent kernels used in spline smoothing. We also examine how weighting patterns are affected by heteroscedasticity and irregular spacing and provide an illustrative example.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1999-44.
Date of creation: 1999
Date of revision:
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Web page: http://center.uvt.nl
Cubic spline; Kalman filter and smoother; Kernels; Robustness; Structural time series model; Trend; Wiener-Kolmogorov filter;
Other versions of this item:
- Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
- 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-1999-07-28 (All new papers)
- NEP-ECM-1999-07-28 (Econometrics)
- NEP-ETS-1999-07-28 (Econometric Time Series)
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