Signal Extraction and the Formulation of Unobserved Components Models

A.C. Harvey, S.J.M. Koopman

Research output: Working paperDiscussion paperOther research output

541 Downloads (Pure)


This 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.
Original languageEnglish
Place of PublicationTilburg
Number of pages28
Publication statusPublished - 1999

Publication series

NameCentER Discussion Paper


  • Cubic spline
  • Kalman filter and smoother
  • Kernels
  • Robustness
  • Structural time series model
  • Trend
  • Wiener-Kolmogorov filter


Dive into the research topics of 'Signal Extraction and the Formulation of Unobserved Components Models'. Together they form a unique fingerprint.

Cite this