Factor analysis

Algorithm

Discrete mathematics

Statistics

Cross-validation

Selection (genetic algorithm)

Computer science

Mathematics

Econometrics

Artificial intelligence

Monte Carlo method

Programming language

Fold (higher-order function)

Consistency (knowledge bases)

Economics Letters

We propose a data driven determination method of the number of factors by cross validation (CV) in approximate factor models. A K-fold CV is applied along each of the two directions (individual and time) of a panel dataset. We prove the consistency of the proposed twice K-fold CV under mild conditions. Monte Carlo simulations demonstrate superior and robust performance of our selection method in comparison with existing approaches, especially at small panels with moderate units or time lengths. An empirical application to identify factor numbers in the UK is provided.

Determining the number of factors in approximate factor models by twice K-fold cross validation