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Course Learning Outcomes: Introductory Econometrics

The Introductory Econometrics course familiarizes students with the econometric methods essential for conducting empirical analysis in Economics. Designed to equip students with fundamental quantitative techniques, it prepares them for applied research projects and lays the foundation for more advanced econometrics courses, enabling deeper exploration of data-driven economic analysis.

Course Modules:

1. Review of Distributions:
Normal Distribution, Z and t distribution, Chi-square distribution and F- Distribution

2. Simple linear regression model: Two variable case
Ordinary least squares estimation of a linear model; properties of estimators; goodness of fit; testing of hypotheses; scaling and units of measurement; confidence intervals; the Gauss Markov theorem; forecasting and prediction

3. Multiple linear regression model:
Extension of the single explanatory variable case to a multivariate setting; introducing non-linearities through functions of explanatory variables.

4. Violations of classical assumptions: Consequences, detection and remedies:
Multicollinearity; heteroscedasticity; serial correlation.

5. Specification Analysis:
Omission of a relevant variable; inclusion of irrelevant variable; specification tests.

NOTE: The above modules give a rough idea about the topics covered in our Introductory Econometrics course. Students will be given modules as per their respective University’s outline after prior discussion.