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Forecasting and signal extraction with misspecified models

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  • Tommaso Proietti

    (Universit� di Udine, Italy)

Abstract

This paper evaluates multistep estimation for the purposes of signal extraction, and in particular the separation of the trend from the cycle in economic time series, and long-range forecasting, in the presence of a misspecified, but simply parameterized model. Our workhorse models are two popular unobserved components models, namely the local level and the local linear model. The paper introduces a metric for assessing the accuracy of the unobserved components estimates and concludes that multistep estimation can be valuable. However, its performance depends crucially on the properties of the series and the paper explores the role of the order of integration and the relative size of the cyclical variation. On the contrary, cross-validation is usually not suitable for the purposes considered. Copyright © 2005 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.970
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 24 (2005)
Issue (Month): 8 ()
Pages: 539-556

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Handle: RePEc:jof:jforec:v:24:y:2005:i:8:p:539-556

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Neil Shephard & Jurgen Doornik & Siem Jan Koopman, 1998. "Statistical algorithms for models in state space using SsfPack 2.2," Economics Series Working Papers 1998-W06, University of Oxford, Department of Economics.
  2. Proietti, Tommaso & Harvey, Andrew, 2000. "A Beveridge-Nelson smoother," Economics Letters, Elsevier, vol. 67(2), pages 139-146, May.
  3. Tommaso Proietti, 2003. "Leave-K-Out Diagnostics In State-Space Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 221-236, 03.
  4. Robert J. Hodrick & Edward Prescott, 1981. "Post-War U.S. Business Cycles: An Empirical Investigation," Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  5. Clements, Michael P. & Hendry, David F., 1996. "Multi-Step Estimation for Forecasting," The Warwick Economics Research Paper Series (TWERPS) 447, University of Warwick, Department of Economics.
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Cited by:
  1. Proietti, Tommaso, 2008. "Band spectral estimation for signal extraction," Economic Modelling, Elsevier, vol. 25(1), pages 54-69, January.
  2. Blöchl, Andreas, 2014. "Penalized Splines as Frequency Selective Filters - Reducing the Excess Variability at the Margins," Discussion Papers in Economics 20687, University of Munich, Department of Economics.
  3. Harvey, Andrew C. & Delle Monache, Davide, 2009. "Computing the mean square error of unobserved components extracted by misspecified time series models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 283-295, February.
  4. Proietti, Tommaso, 2008. "Structural Time Series Models for Business Cycle Analysis," MPRA Paper 6854, University Library of Munich, Germany.
  5. Göran Kauermann & Timo Teuber & Peter Flaschel, 2012. "Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression," Computational Economics, Society for Computational Economics, vol. 39(4), pages 409-427, April.
  6. Pollock, D.S.G., 2006. "Introduction to the special issue on statistical signal extraction and filtering," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2137-2145, May.

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