Common correlated effect cross-sectional dependence corrections for non-linear conditional mean panel models

This paper provides an approach to estimation and inference for non-linear conditional mean panel data models, in the presence of cross-sectional dependence. We modify the common correlated effects (CCE) correction of Pesaran (2006) to filter out the interactive unobserved multifactor structure. The estimation can be carried out using non-linear least squares, by augmenting the set of explanatory variables with cross-sectional averages of both linear and non-linear terms. We propose pooled and mean group estimators, derive their asymptotic distributions, and show the consistency and asymptotic normality of the coefficients of the model. The features of the proposed estimators are investigated through extensive Monte Carlo experiments. We apply our method to estimate UK banks’ wholesale funding costs and explore the non-linear relationship between public debt and output growth.