Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose

Authors


  • We would like to thank Dante Amengual, Marco Capasso, Marc Hallin, Roman Liska and Uta Pigorsch for providing their codes, Christian Schumacher for sending the German database, Clément Marsilli for excellent research assistance, as well as the participants of the 29th International Symposium on Forecasting, San Diego, US, June 2010, and the 6th Colloquium on Modern Tools for Business Cycle Analysis, Luxembourg, September 2010. In addition, we would like to thank two anonymous referees for helpful remarks. The views expressed herein are those of the authors and do not necessarily reflect those of the Banque de France and the International Monetary Fund.

Abstract

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.

Ancillary