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Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose *
Author(s) -
Barhoumi Karim,
Darné Olivier,
Ferrara Laurent
Publication year - 2013
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12010
Subject(s) - nowcasting , overtime , econometrics , dynamic factor , set (abstract data type) , order (exchange) , computer science , stability (learning theory) , empirical research , economics , statistics , mathematics , machine learning , meteorology , finance , labour economics , programming language , physics
GDP forecasts based on dynamic factor models, applied to a large data set, are now widely used by practitioners involved in nowcasting and short‐term macroeconomic forecasting. One recurrent empirical question that arises when dealing with such models is the way to determine the optimal number of factors. At the same time, statistical tests have recently been put forward in the literature in order to optimally determine the number of significant factors. In this article, we propose to reconcile both fields of interest by selecting the number of factors, through a testing procedure, to include in the forecasting equation. Through an empirical exercise on French and German GDPs, we assess the impact of a battery of recent statistical tests for the number of factors for a forecasting purpose. By implementing a rolling experience, we also assess the stability of the results overtime.