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14 - Turning point prediction with the composite leading index: An ex ante analysis

Published online by Cambridge University Press:  05 June 2012

Francis X. Diebold
Affiliation:
University of Pennsylvania
Glenn D. Rudebusch
Affiliation:
Division of Research and Statistics Board of Governors
Kajal Lahiri
Affiliation:
State University of New York, Albany
Geoffrey H. Moore
Affiliation:
Columbia University, New York
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Summary

On the day of its release, the preliminary estimate of the Department of Commerce composite index of leading indicators (CIA) is widely reported in the popular and financial press. Although declines in the composite leading index are often regarded as a potential signal of the onset of a recession, evaluations of the ability of the CLI to predict turning points have been limited in most previous studies by the use of final, revised CLI data. However, the composite leading index is extensively revised after each preliminary estimate; not only are revisions made as more complete historical data become available for the components, but ex post, the statistical weights are updated and components are added or eliminated to improve leading performance. Forecasts constructed with an ex post, recomputed CLI may differ from real-time forecasts based on the contemporaneous, original construction CLI. In this chapter, we perform a completely ex ante, or real-time, evaluation of the ability of the CLI to predict turning points by using the original preliminary estimates and revisions as they became available in real time.

In section 14.1, we describe revisions in the CLI and our procedure for generating ex ante turning point probability forecasts from the CLI. The methodology is the Bayesian procedure described in Diebold and Rudebusch (1989), adapted to a newly constructed ex ante dataset. This new dataset, which has over 70,000 elements, contains every preliminary, provisionally revised, and final estimate of the CLI since the inception of the index in 1968.

Type
Chapter
Information
Leading Economic Indicators
New Approaches and Forecasting Records
, pp. 231 - 256
Publisher: Cambridge University Press
Print publication year: 1991

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