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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 24 (2005)
Issue (Month): 8 ()
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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
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