Estimation of the common component in Dynamic Factor Models

One of the most effective techniques that allows a low-dimensional representation of Big Datasets is the Dynamic Factor Model (DFM). We analyze the finite sample performance of the well-known Principal Component estimator for the common component under different scenarios. Simulation results show that for data samples with large number of observations and small time series dimension, the variance-covariance matrix specification with lags provides better estimations than the classic variance-covariance matrix. However, in high-dimension data samples the classic variance-covariance matrix performs better no matter the sample size. Second, we apply the Principal Component estimator to obtain estimates of the business cycles of the Euro Area and its country members. This application, together with a cluster analysis, studies the phenomenon known as the Two-Speed Europe with two groups of countries not geographically related.