Estimation of Dynamic Mixed Double Factors Model in High Dimensional Panel Data

TL;DR

This paper introduces the Dynamic Mixed Double Factor Model (DMDFM) for high-dimensional panel data, combining PCA and GMM to reduce dimensionality, account for cross-sectional and time series effects, and improve prediction accuracy.

Contribution

The paper develops the DMDFM, a novel model that integrates mixed factor structures with dynamic panel data analysis, enhancing dimension reduction and predictive performance.

Findings

GMM estimators show good unbiasedness and consistency.

DMDFM improves prediction power effectively.

Simulation results validate the model's effectiveness.

Abstract

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 minority common factors in the factor models. We propose the Dynamic Mixed Double Factor Model (DMDFM for short) to re ect 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 re flect common trend and reduce the dimension, error components factors re ect difference and weak correlation of individuals. The results of Monte Carlo simulation show that Generalized Method of Moments (GMM) estimators have good unbiasedness and consistency. Simulation also shows that the DMDFM can improve prediction power of the models effectively.