Abstract
•Propose model to allow firm heterogeneity and inefficiency.•Semiparametric in nature, no distributional assumptions required.•Provide data-driven bandwidth selection mechanism.•Simulations reveal excellent finite sample properties.
We consider the application of the profile least-squares method to estimate the impact of the determinants of inefficiency in the presence of panel data and unobserved individual specific heterogeneity for the stochastic frontier model. This method has the advantage over previous approaches in that the effect of the determinants of inefficiency on output can be recovered nonparametrically in the presence of heterogeneity at the firm level. We describe the estimator and offer a small set of Monte Carlo exercises that showcases the value of the method for the stochastic frontier model. Application of the method to assess insights generated compared to a pooled cross-section approach using data from the Taiwan banking system is also provided.
