A PRACTICAL VARIABLE SELECTION METHOD FOR GENERALIZED LEAST SQUARES AND ITS APPLICATION

Abstract

The j-th best subset problem for the generalized least squares is formulated in which statistical criteria as well as non-statistical conditions are introduced. Non-statistical conditions are based on a knowledge of the scientific field to which research is related, natural logic and common sense, while statistical criteria are t-test, Durbin-Watson serial correlation test, absolute relative error test, turning point test and fitting test, depending on the covariance matrix of a disturbance term and type of data. Various technical methods are devised to make a computer solve the first (j=1) to the J-th (e.g., J=10) best subset problems in one computer-run, depending on whether or not a researcher has a new criterion or appropriate values for the parameters used to evaluate meaningful subsets before estimation. Then, the ultimately best subset among the best J subsets is regarded as a practical solution to the variable selection problem for the generalized least squares. The System OEPP can handle the proposed variable selection method.