Robust estimation of panel data regression models and applications

Abstract

The common parameter estimation methods of panel data linear model include least square dummy variable estimation, two-stage least square estimation, quasi-maximum likelihood estimation and generalized moment estimation. However, these estimation methods are not robust and are easily affected by outliers. Firstly, this paper extends support vector regression algorithm to fit several parallel super-plane simultaneously and provide a novel robust estimation of fixed-effect panel data linear model; then using the kernel trick, a robust estimation for fixed effect panel data nonlinear model is introduced. Finally, the proposed model (linear or nonlinear) is applied in forecasting air quality index of the cities of Jing-Jin-Ji district in China. Experiments shows that our proposed model are robust and have good generalization performance.

Keywords:

• Robust estimation
• support vector regression
• fixed effect panel data model
• air quality index forecasting

JEL CLASSIFICATION:

• C23

Acknowledgments

This work was supported by a grant from Hebei province Social Science Fund (HB18GL014).