Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion

This paper focuses on the determination of the number of change-points in high-dimensional factor models via cross-validation with matrix completion. An imputed method is proposed to predict the validation data set which is seen as the “missing” data of the training set. The number of change-points can be determined by minimizing the prediction error on the validation set. The consistency of the estimator is established under some mild conditions. Monte Carlo simulation results show desired performance of the proposed method compared to the existing competitors.