 # Introduction to Econometrics

Course TypeCourse CodeNo. Of Credits
Foundation CoreSUS1EC1114

Semester and Year Offered: 6th Semester and 3rd Year

Course Coordinator and Team: Krishna Ram and Saranika Sarkar

Email of course coordinator: Krishna[at]aud[dot]ac[dot]in and saranika[at]aud[dot]ac[dot]in

Pre-requisites: Students should have taken “Statistical Methods for economics”.

Aim: This course shall introduce the approach of unifying the theoretical and empirical dimensions of economic analysis. Basic econometric methods like simple and multiple linear regression analysis will be discussed with an emphasis on their applications and the issues that have to be confronted in that process.

Course Outcomes:

After completing this course successfully the students will be able to:

1. Explain the difference between correlation and regression, and uses of different kinds of data set in regression analysis.
2. Estimate & interpret linear regression model using ordinary least square method, and conduct hypothesis testing of estimated coefficients.
3. Identify different functional form of linear regression models, conduct diagnostic testing, and suggest remedial measure if required.
4. Explain relevance of assumptions of CLRM, and its consequences on regression model if the model violates any of the listed assumptions.
5. Perform misspecification test such as test for omission of relevant explanatory variable, test for inclusion of irrelevant explanatory variable, and other misspecification test using econometric software package STATA
6. Evaluate the empirical research papers that have used the concepts and statistical methods introduced in this course.
7. Use statistical software such as Stata or Gretl using real world data for their own empirical research.

Brief description of modules/ Main modules:

1. Nature and Scope of Econometrics
2. Review of Statistical Concepts: Normal distribution; chi-sq, t- and F-distributions; estimation of parameters; properties of estimators; testing of hypotheses.
3. Simple (two variable) Linear Regression Model: Estimation of model by method of ordinary least squares; properties of estimators; goodness of fit; tests of hypotheses;scaling and units of measurement; Gauss-Markov theorem; forecasting.
4. Multiple Linear Regression Model: Estimation of parameters; properties of OLS estimators; goodness of fit - R2 and adjusted R2; partial regression coefficients; testing hypotheses – individual and joint; functional forms of regression models; qualitative (dummy) independent variables.
5. Violations of Classical Assumptions: Consequences, Detection and Remedies: Multicollinearity; heteroscedasticity; serial correlation.
6. Specification Analysis: Omission of a relevant variable; inclusion of irrelevant variable; tests of specification errors.
7. Computer Lab sessions using Excel, Gretl/Stata.

Assessment Details with weights:

 S.No Assessment Date/period in which Assessment will take place Weightage 1 Written test 1 MidFebruary 25% 2 Lab test End of March 25% 3 Written test 2 April 25% 4 End Semester Exam As per AUD Academic Calendar 25%