Abstract
For chapter one, 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.
For chapter two, in this paper we propose a new bandwidth selection method that is helpful to get a good estimation when contamination is in the data. Our method belongs within the field of cross-validation and picks up both the optimal bandwidths and the resistance parameters such as the parameter $c$ in Huber's function or variable in the new shape function. Asymptotic properties of our method are provided, as well as descriptions of how to select the resistance function by Monte-Carlo simulation results. Two applications are provided in the paper.