Efficient estimation of a partially linear panel data model with cross-sectional dependence

Abstract

This paper considers efficiency improvements in a partially linear panel data model that accounts for possible nonlinear effects of common covariates and allows for cross-sectional dependence arising simultaneously from unobserved common factors and spatial dependence. A generalized least squares-type estimator is proposed by taking into account this dependence structure. Also, possible gains in terms of the rate of convergence are studied. A Monte Carlo study is carried out to investigate the proposed estimators finite sample performance. Further, an empirical application is conducted to assess the impact of the carbon price linked to the European Union Emission Trading System on carbon dioxide emissions.