The purpose of this article is to develop the dimension reduction techniques in panel data analysis when the number of individuals and indicators is large. We use Principal Component Analysis (PCA) method to represent large number of indicators by mi-nority 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 interactive factor structure. DMDFM not only reduce the dimension of indicators but also consider the time series and cross section mixed effect. Different from other models, mixed factor model have two styles of common factors. The regressors factors reflect common trend and reduce the dimension, error components factors reflect differ-ence and weak correlation of individuals. The results of Monte Carlo simulation show that Generalized Method of Moments (GMM) estimators have good unbiasedness and