This course provides basic econometric concepts, understanding through software. It covers estimation and diagnostic testing of simple and multiple regression models.
At the end of the course, students should be able to:
Unit I (3 Weeks)
Introduction to Econometrics and an overview of its applications; Simple Regression with Classical Assumptions; Least Square Estimation And BLUE, Properties of estimators, Multiple Regression Model and Hypothesis Testing Related to Parameters – Simple and Joint.
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [ Chapter 1-9]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [Chapter1-3]
Unit II (3 Weeks)
Assumptions Violations; understanding of assumptions, what is the consequences if violated, their identification, how to take care.
• Zero Mean of error
• Homoscedasticity
• Autocorrelation
• Uncorrelatedness of regressor and disturbance
• Normality
• Non-Stochastic Regressor
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 10-13]
Gujarati, N. Damodar. Econometrics by Examples. New Delhi: McGraw Hill. [Chapter 4-7]
Unit III (3 Weeks)
Outliers & Influential Points; understanding, deduction and how to take care. goodness of fit.
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 3 and 4]
Unit IV (3 Weeks)
How to report results of the Regression?
How to decide which Regression Model provides a better fit?
Interpretation of functional forms of regression model.
Scaling effect in Regression …
Point and Interval Estimation of parameters of the Regression Model
References:
Gujarati, N. Damodar. Basic Econometrics. New Delhi: McGraw Hill. [Chapter 5]
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