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Computing Observation Weights for Signal Extraction and Filtering

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Author Info
A. C. Harvey
Siem Jan Koopman (Free University of Amsterdam)

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Abstract

We present algorithms for computing the weights implicitly assigned to observations when estimating unobserved components using a model in state space form. The algorithms are for both filtering and signal extraction. In linear time-invariant models such weights can sometimes be obtained analytically from the Wiener-Kolmogorov formulae. Our method is much more general, being applicable to any model with a linear state space form, including models with deterministic components and time-varying state matrices. It applies to multivariate models and it can be used when there are data irregularities, such as missing observations.
The algorithms can be useful for a variety of purposes in econometrics and statistics: (i) the weights for signal extraction can be regarded as equivalent kernel functions and hence the weight pattern can be compared with the kernels typically used in nonparametric trend estimation; (ii) the weight algorithm for filtering implicitly computes the coefficients of the vector error-correction model (VECM) representation of any linear time series model; (iii) as a by-product the mean square errors associated with estimators may be obtained; (iv) the algorithm can be incorporated within a Markov chain Monte Carlo (MCMC) method enabling computation of weights assigned to observations when computing the posterior mean of unobserved components within a Bayesian treatment.
A wide range of illustrations show how the algorithms may provide important insights in empirical analysis. The algorithms are provided and implemented for the software package SsfPack 2.3 , that is a set of filtering, smoothing and simulation algorithms for models in state space form (see www.ssfpack.com). Some details of implementation and example programs are given in the appendix of the paper.

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Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 0888.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:0888

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Andrew Harvey & Chia-Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal Of The Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309. [Downloadable!] (restricted)
  2. Peter Burridge & Kenneth Wallis, 1988. "Prediction theory for autoregressivemoving average processes," Econometric Reviews, Taylor and Francis Journals, vol. 7(1), pages 65-95. [Downloadable!] (restricted)
  3. Balke, Nathan S, 1993. "Detecting Level Shifts in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 81-92, January.
  4. Harvey, A. & Koopman, S.J., 1999. "Signal extraction and the formulation of unobserved components models," Discussion Paper 44, Tilburg University, Center for Economic Research. [Downloadable!]
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  5. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The effect of the great moderation on the U.S. business cycle in a time-varying multivariate trend-cycle model," Working Papers UWEC-2008-15, University of Washington, Department of Economics. [Downloadable!]
    Other versions:
  2. Andrés González Gómez & Lavan Mahadeva & Diego Rodríguez & Luis Eduardo Rojas, . "Monetary Policy Forecasting in a DSGE Model with Data that is Uncertain, Unbalanced and About the Future," Borradores de Economia 559, Banco de la Republica de Colombia. [Downloadable!]
    Other versions:
  3. Roberto Iannaccone & Edoardo Otranto, 2003. "Signal Extraction in Continuous Time and the Generalized Hodrick- Prescott Filter," Econometrics 0311002, EconWPA. [Downloadable!]
  4. Harvey, A. & Koopman, S.J., 1999. "Signal extraction and the formulation of unobserved components models," Discussion Paper 44, Tilburg University, Center for Economic Research. [Downloadable!]
    Other versions:
  5. Siem Jan Koopman & Soon Yip Wong, 2008. "Spline Smoothing over Difficult Regions," Tinbergen Institute Discussion Papers 08-114/4, Tinbergen Institute. [Downloadable!]
  6. Fabio Busetti, 2001. "The use of preliminary data in econometric forecasting: an application with the Bank of Italy Quarterly Model," Temi di discussione (Economic working papers) 437, Bank of Italy, Economic Research Department. [Downloadable!]
  7. Harvey, A.C. & Trimbur, T.M., 2001. "General Model-based Filters for Extracting Cycles and Trends in Economic Time Series," Cambridge Working Papers in Economics 0113, Faculty of Economics, University of Cambridge. [Downloadable!]
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  8. Roberta Zizza, 2006. "A measure of output gap for Italy through structural time series models," Journal of Applied Statistics, Taylor and Francis Journals, vol. 33(5), pages 481-496, June. [Downloadable!] (restricted)
  9. Elena Angelini & Gonzalo Camba-Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2008. "Short-Term Forecasts of Euro Area GDP Growth," ECARES Working Papers 2008_035, Université Libre de Bruxelles, Ecares. [Downloadable!]
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  10. Marta Banbura & Gerhard Rünstler, 2007. "A look into the factor model black box - publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 751, European Central Bank. [Downloadable!]
  11. Tommaso Proietti, 2006. "Measuring Core Inflation by Multivariate Structural Time Series Models," CEIS Research Paper 83, Tor Vergata University, CEIS. [Downloadable!]
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