Forecasting and Signal Extraction with Misspecified Models

Tommaso Proietti ()

Econometrics from EconWPA

Abstract: The paper illustrates and compares estimation methods alternative to maximum likelihood, among which multistep estimation and leave-one-out cross-validation, 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 parameterised 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 cross- validation is not a suitable estimation criterion for the purpose considered, whereas multistep estimation can be valuable. Finally, we propose a local likelihood estimator in the frequency domain that provides a simple and alternative way of making operative the notion of emphasising the long-run properties of a time series.

Keywords: Business cycles; Unobserved components models; Cross- validation; Smoothing; Hodrick-Prescott filter; Multistep estimation. (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2004-01-07
Note: Type of Document - ; prepared on WinXP; pages: 34; figures: 9
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Related works:
Journal Article: Forecasting and signal extraction with misspecified models (2005)
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0401002

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