In the Present book Chapter -I contains the general introduction about the model selection. Chapter-II speaks about the various inferential problems of linear statistical models. The distributional properties of the OLS estimators have been presented in this chapter. It also contains the procedure of testing the general linear hypothesis .Chapter-III deals with the existing criteria for model selection given in the literature. Chapter-IV proposes some new techniques for selection of linear statistical models pertaining both the nested and non-nested linear regression models. Chapter-V epitomizes the conclusions. Various selected references have been documented under a separate caption 'Bibliography'.

In this present book Chapter-I is an introductory one. Chapter-II describes the various criteria for selection of regressors in the multiple regression analysis existing in this book. Chapter-III deals with the basic stepwise regression procedures for variable selection in multiple regression analysis and The mean square error of prediction criterion has been discussed along with a similar average estimated variance criterion for the selection of variables in the general linear model. Chapter-IV presents the various methods for choosing variable subsets in multiple linear regression analysis under these methods, the mean squared prediction error has been considered as basis of the criteria. Chapter-V proposes some new criteria for selection of regressors in econometrics based on different types of residuals such as Ordinary Least Squares, Studentized and Predicted residuals. Chapter-VI depicts the main conclusions of the present research study. It also narrates the plan for future research as an extension in the lines of study. Several relevant references have been documented under a separate title "BIBLIOGRAPHY."