Penalized quantile regression for spatial panel data with fixed effects

This paper studies a penalized quantile regression for spatial panel model with fixed effects, where a penalized method is used to control additional variability by shrinking the individual fixed effects to a common value with a tuning parameter. The bias is reduced by employing the instrumental variables quantile regression method. Limiting properties of the proposed estimator are derived, say consistency and asymptotic normality. Monte Carlo simulations results are provided to illustrate the finite sample behavior of the proposed estimation methods. The use of the procedures is illustrated through an empirical application to analyze a spatial panel data related to military expenditure from 144 countries over 15 years.