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Forecasting real-time data allowing for data revisions

✍ Scribed by Kosei Fukuda

Publisher
John Wiley and Sons
Year
2007
Tongue
English
Weight
220 KB
Volume
26
Category
Article
ISSN
0277-6693

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✦ Synopsis

Abstract

A modeling approach to real‐time forecasting that allows for data revisions is shown. In this approach, an observed time series is decomposed into stochastic trend, data revision, and observation noise in real time. It is assumed that the stochastic trend is defined such that its first difference is specified as an AR model, and that the data revision, obtained only for the latest part of the time series, is also specified as an AR model. The proposed method is applicable to the data set with one vintage. Empirical applications to real‐time forecasting of quarterly time series of US real GDP and its eight components are shown to illustrate the usefulness of the proposed approach.  Copyright Β© 2007 John Wiley & Sons, Ltd.

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