### Abstract

The idea of factor analysis is that it can use a few latent factors to capture the variations of a large number of economic variables in a high dimensional data set. A critical question in factor analysis is to estimate the number of factors. Most methods for choosing the number of factors are based on the results from random matrix theory (RMT), which studies the distribution of sample eigenvalues and requires i.i.d and gaussian assumption on the error terms in the factor model. These restrictions may not appropriate when we want to apply those methods in practice. This paper aims to show that those methods are not robust by simulation when the error terms in the factor model are serially and cross-sectionally correlated or have non-gaussian distributions. Our simulation results provide useful recommendations to applied users for how to choose the estimation method in dealing with different types of data.

###### Ph.D. Candidate in Economics

My research interests include econometrics, causal inference, financial econometrics, machine learning, and forecasting.