Nonstationary factor model applications of Elastic Net

Abstract

In this thesis, we adopted Elastic Net estimators for selecting true number of factors in factor models with stationary and nonstationary factors. Elastic Net is a member of shrinkage estimators family. As a member of shrinkage estimators family, elastic net estimators are stable to changes in data and in general they do not over parametrize the models. These two properties of elastic net estimators makes elastic net more favourable than information based criterion penalty methods for estimating true factor number. Since Principal Components Analysis (PCA) based algorithms always tends to give only single factor for nonstationary data sets, we use Sparse Principal Components Analysis (SPCA) algorithm which is a regression-type optimization formulation of PCA. Simulations show the performance of Elastic Net estimator for estimation of true factor number with stationary and nonstationary factors cases .