A Comparative Study of Generalized Maximum Entropy Estimator for the Two-way Error Component Regression Model

Abstract

Recently the study of the panel data has received attention in the literature of the regression model. The model has been usually dealing with the complete data. However, in a practical manner it is rare for data to be complete. For ill-posed problems, Song and Cheon(2006) proposed a robust generalized maximum entropy estimator less sensitive to the assumption and limited situation in a panel regression model with the only individual effect. However, the time effect needs to be considered in panel data. This paper considers a two-way error component model with both individual and time effects in ill-posed problems and proposes the generalized maximum entropy(GME) estimator for the unknown parameters. This estimator is compared with a variety of existing estimators on the simulated dataset. The numerical results are in favor of the new estimator in terms of its quality when the data are ill-posed.