A Testing Procedure for Determining the Number of Factors in Approximate Factor Models with Large Datasets

The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be adequately modelled by a small number of indicator variables. Throughout this extensive research activity on large dimensional factor models a major preoccupation has been the development of tools for determining the number of factors needed for modelling. This paper provides builds on the work of Kapetanios (2004) to provide an alternative method to information criteria as a tool for estimating the number of factors in large dimensional factor models. The new method is robust to considerable cross-sectional and temporal dependence. The theoretical properties of the method are explored and an extensive Monte Carlo study is undertaken. Results are favourable for the new method and suggest that it is a reasonable alternative to existing methods.