Estimation of dynamic mixed double factors model in high-dimensional panel data

This paper endeavors to develop some dimension reduction techniques in panel data analysis when the numbers of individuals and indicators are very large. We use principal component analysis method to represent a large number of indicators via minority common factors in the factor models. We propose the dynamic mixed double factor model (DMDFM for short) to reflect cross section and time series correlation with the interactive factor structure. DMDFM not only reduces the dimension of indicators but also deals with the time series and cross section mixed effect. Different from other models, mixed factor models have two styles of common factors. The regressors factors reflect common trend and the dimension reducing, while the error components factors reflect difference and weak correlation of individuals. The results of Monte Carlo simulation show that generalized method of moments estimators have good properties of unbiasedness and consistency. Simulation results also show that the DMDFM can improve the prediction power of the models effectively.