In the book an attempt has been made to develop some new diagnostic tests for statistical model building in the context of linear regression models. Diagnostics of outliers has been described and a test for identifying outliers by using predicted residuals has been proposed in the present study. Besides outliers, a measure of influence for diagnostics has been developed in the study. Some new modified R2 and criteria for model selection have been developed along with modified mean square prediction error criteria; and modified information criteria for model selection. A test for exogeneity in model specification by using augmented regression model; and a test for stability of regression parameters in model specification have been proposed under diagnostic tests. A new test for misspecification of the linear regression model has been derived along with a modified Rainbow Test by using Internally studentized residuals. The problem of misspecification of non-nested linear regression models, has been discussed together with a diagnostic test for functional form between loglinear and linear regression models.

This book attempts to develope some new inferential procedures for time series regression models.An inferential method for a time series linear regression model with auto correlated disturbances using quarterly data, has been developed by proposing a test based on internally studentized residuals.Two modified estimation procedures have been proposed for time series regression models involving MA (1) and MA (q) process errors.Autoregressive moving averages and autoregressive conditionally heteroscadastic (ARCH) processesses have been specified systematically with their characteristics. The generalized ARCH model is specified and the effect of error structure on ARCH model has been explained. Two modified tests for detecting the problem of ARCH errors have been developed by using Box-pierce-lying test statistics based on internally studentized residuals. A new estimation procedure has been developed for ARCH model by using an interactive technique

The bench mark to measure potential output is the data envelopment frontier determined by the best practice production units, which in the language of Data Envelopment Analysis (DEA) are called Decision Making Units (DMU's).In this book Chapter-I contains about the measurement of technical change in terms of total factor productivity growth and the contribution inputs growth to output growth. Chapter-II deals three important approaches namely, Parametric, Non-Parametric and Accounting approaches have been discussed for the measurement of technical change and technical progress. Chpater-III proposes some theoretical methods to decompose the Malmquist total factor productivity index into different efficiency changes. Chapter-IV presents the empirical investigation regarding the present research study. The various efficiency changes such as pure technical, output technical, scale efficiency changes have been completed for different states in India for the study periods . Chpater-V depicts the summary of results and important conclusions of the present work finaly selected references regarding present book have been given under a separate title "Bibliography."