This paper presents a new forecasting approach straddling the conventional methods applied to the Italian industrial production index. Specifically, the proposed method treats factor models and bridge models as complementary ingredients feeding a unique model specification. We document that the proposed approach improves upon traditional bridge models by making efficient use of the information conveyed by a large amount of survey data on manufacturing activity. Different factor algorithms are compared and, under the provision that a large estimation window is used, partial least squares outperform principal component-based alternatives. Copyright © 2016 John Wiley & Sons, Ltd.

Factor-Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production

Guardabascio B.;
2016

Abstract

This paper presents a new forecasting approach straddling the conventional methods applied to the Italian industrial production index. Specifically, the proposed method treats factor models and bridge models as complementary ingredients feeding a unique model specification. We document that the proposed approach improves upon traditional bridge models by making efficient use of the information conveyed by a large amount of survey data on manufacturing activity. Different factor algorithms are compared and, under the provision that a large estimation window is used, partial least squares outperform principal component-based alternatives. Copyright © 2016 John Wiley & Sons, Ltd.
2016
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1553715
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