Premium
Factor‐Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production
Author(s) -
Girardi Alessandro,
Guardabascio Barbara,
Ventura Marco
Publication year - 2016
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2393
Subject(s) - bridge (graph theory) , computer science , principal component analysis , partial least squares regression , factor (programming language) , production (economics) , industrial production , factor analysis , soft sensor , dynamic factor , econometrics , index (typography) , estimation , data mining , artificial intelligence , machine learning , mathematics , economics , process (computing) , medicine , world wide web , keynesian economics , macroeconomics , programming language , operating system , management
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom