This paper proposes a quantile regression estimator for a model with interactive effects potentially correlated with covariates. We provide conditions under which the estimator is asymptotically Gaussian and we investigate the finite sample performance of the method. An approach to testing the...

This paper proposes an ℓ1 penalized quantile regression estimator which adapts the Hausman–Taylor instrumental variable approach in order to address the bias resulting from the shrinkage of the individual effects.

We introduce a quantile regression approach to panel data models with endogenous variables and individual effects correlated with the independent variables. We find newly developed quantile regression methods can be easily adapted to estimate this class of models efficiently.

We propose a method for estimating the slope parameter in an interactive effects panel data model with endogenous loadings and factors, and endogenous regressors.

Policy makers rely on a mix of government spending and tax cuts to address the imbalances in the economy during an economic crisis, by promoting price stability and renewed economic growth. However, little discussion appears to focus explicitly on quantifying the cost of economic crises in terms...

This paper proposes new ℓ1-penalized quantile regression estimators for panel data, which explicitly allows for individual heterogeneity associated with covariates. We conduct Monte Carlo simulations to assess the small sample performance of the new estimators and provide comparisons of new...

This paper proposes a quantile regression estimator for a panel data model with interactive effects potentially correlated with the independent variables. We provide conditions under which the slope parameter estimator is asymptotically Gaussian. Monte Carlo studies are carried out to...