Non-spherical disturbances, the generalized least squares. Multicollinearity , Ridge Regression, Heteroscedasticity and their solution. Problems of Auto Correlation
System of simultaneous linear equations, identification problem, rules of identification, estimation methods,instrumental variables, lagged variables, Dummy Variables.
To produce the ability to build regression model in the violation of assumption when ordinary least square method is not appropriate and to learn which method is more preferable in certain given conditions.
Course Learning Outcomes
The learning outcomes associated with course are aimed at students being able to
• deal with problem of multicollinearity.
• estimate parameters in regression equations when the random disturbances are allowed to be heteroskedastic or auto-correlated
• use an appropriate econometric model for a real data problem, interpretation and critical evaluation of the outcomes of empirical analysis
• be able to report the results of their work in a non-technical and literate manner
Dummy variables - an introduction by Ben Lambert
Statistics 101: Multiple Linear Regression, Two Categorical Variables by Brandon Foltz
Conduct ing a Multiple Regression After Dummy Coding Variables in SPSS by Dr. Todd Grande
Book Title : Basic Econometrics
Author : Demondar N. Gujrati
Edition : 4th Edition
Publisher : McGraw Hill Book Company
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