Forecasting and signal extraction with misspecified models
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 24 (2005)
Issue (Month): 8 ()
Contact details of provider:
Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
Other versions of this item:
- Tommaso Proietti, 2004. "Forecasting and Signal Extraction with Misspecified Models," Econometrics 0401002, EconWPA.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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.:
- 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.
- 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.
- Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Discussion Paper 1998-141, Tilburg University, Center for Economic Research.
- Proietti, Tommaso & Harvey, Andrew, 2000. "A Beveridge-Nelson smoother," Economics Letters, Elsevier, vol. 67(2), pages 139-146, May.
- 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.
- Robert J. Hodrick & Edward Prescott, 1981.
"Post-War U.S. Business Cycles: An Empirical Investigation,"
451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
- 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.
- Proietti, Tommaso, 2008.
"Band spectral estimation for signal extraction,"
Elsevier, vol. 25(1), pages 54-69, January.
- 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.
- 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.
- Proietti, Tommaso, 2008.
"Structural Time Series Models for Business Cycle Analysis,"
6854, University Library of Munich, Germany.
- Tommaso Proietti, 2008. "Structural Time Series Models for Business Cycle Analysis," CEIS Research Paper 109, Tor Vergata University, CEIS, revised 10 Jul 2008.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.