Robust regression, HCCM estimators, and an Empirical Bayes application

Abstract

This Ph.D. thesis includes three topics of econometrics where the chapters of the whole study are devoted to robust regression analysis, research on the estimators for the covariance matrix of a heteroskedastic regression and finally an application of the Empirical Bayes method to some real data from Istanbul Stock Exchange. Some robust regression techniques are applied to some data sets to show how outliers of a data set may lead to wrong inferences. The results reveal that the former studies have gone through some wrong results with the effect of the outliers that were not detected. Second chapter makes a thorough evaluation of the existing heteroskedasticity consistent covariance matrix estimators where the Maximum Likelyhood estimator recently promoted to the literature by Zaman is also taken into consideration. Finally, some empirical study is carried out in the last part of the thesis. The firms of ISE are categorized into sectors and some estimation is done over an equation which is very common and simple in the finance literature.