डॉ. बी. आर. अम्बेडकर विश्‍वविद्यालय दिल्ली
Dr. B. R. Ambedkar University Delhi
Established by The Government of NCT of Delhi

Course coordinator and team:  Saranika Sarkar  & Krishna Ram

1.       Does the course connect to, build on or overlap with any other courses offered in AUD?

The course builds on Econometrics -I (offered in the 1^st semester) MA students at AUD.  

2.       Specific requirements on the part of students who can be admitted to this course:

(Pre-requisites; prior knowledge level; any others – please specify)

Knowledge of mathematics (especially Linear Algebra and Calculus), statistics, and Introductory econometrics is required.  

3.       No. of students to be admitted (with justification if lower than usual cohort size is proposed):

As per SUS norms.

4.       Course scheduling (semester; semester-long/half-semester course; workshop mode; seminar mode; any other – please specify):

It is a semester-long course to be offered in the second semester

5.       How does the course link with the vision of AUD?

The course aligns seamlessly with AUD’s vision of cultivating postgraduate Economic students proficient in tools and techniques used in data analysis. It forms a foundational element in a two year postgraduate programme in Economics,  delivering essential econometric knowledge and skills.

 

 

6.       How does the course link with the specific programme(s) where it is being offered?

The course acquaints students with econometric theory, as well as implementation of the theory using real-world data and software applications like STATA/R. It offers students an understanding of econometrics tools and techniques essential for analyzing real-world economic problems.

Course Details:

a.     Summary:

It is a tool course which is designed to equip students for analysing real-life data related to economics in particular and social science in general, with the help of mathematical knowledge and computer software. In today’s world, students are required to analyse the problem at hand objectively, and this is true in social sciences as well. This course will acquaint the students with theoretical knowledge as well as implementation of theory through software applications like STATA/R. The main thrust of the course will be learning econometric techniques that are used in cross-section data analysis.

 

b.     Objectives:

1.     To equip students for analysing real-life data, related to economics in particular and social science in general, with the help of mathematical knowledge and computer software

2.     To acquaint the students with theoretical knowledge as well as implementation of theory through software applications like STATA/R

3.     To provide valuable skills in data analysis particularly relevant to economics and social sciences and help them to become proficient in handling big data in these fields. 

4.     To estimate causal relationships between variables.

c.     Expected learning outcomes: 

Upon successful completion of this course students will be able to:

1.     Use the matrix approach of OLS estimation and do hypothesis testing.

2.     Estimate multivariate linear regression model using cross-sectional data set.

3.     Identify all possible misspecifications in linear regression model using econometric software package STATA/R.

4.     Use the instrument variable technique for the model estimation.

5.     Estimate categorical dependent variable regression model.

d.     Overall structure (course organisation, rationale of organisation; outline of each module):

The course consists seven main modules :

1.     Linear Regression Model:  Matrix Approach to Regression Models, Estimation, Finite sample properties of OLS estimator &Hypothesis Testing

2.     Asymptotic properties of the OLS estimator

3.     Multicollinearity, Heteroscedasticity and Autocorrelation

4.     Regression Modelling: Variable transformations, Functional forms, Omitted variable bias, and Measurement errors

5.     Instrumental Variable Regression Model

6.     Maximum Likelihood Method

7.     Logit/Probit Regression Models

 

e.     Contents (week-wise plan with readings):

The main textbooks for the course will be:

 

W. H. Greene (2018) : Econometric Analysis, 8^th ed,  Appendix-A, B and C, Ch-1-8,

J. Wooldridge (2010): Cross Section and Panel Data Regression, 2^nd ed. MIT Press, Ch-1-5,15

[DM] Davidson, R., & MacKinnon, J. G. (2004). Econometric theory and methods, New York: Oxford  University Press. Ch-1 -5

J. Scott Long(1997). Regression Model for Categorical and Limited Dependent Variable, SAGE pub.

[ Wooldridge- Introductory]  J. Wooldridge (2019). Introductory Econometrics: A Modern Approach, 7^th ed , Cengage pub. Ch- 15

Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press. Ch-3

Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press. Ch- 4

Week*	Plan/ Theme/ Topic	Core Reading (with chapter no.)	Additional Suggested Readings	Assessment (weights, modes, scheduling)
1-3	Linear Regression Model:  Matrix Approach to Regression, Estimation, Interpretation, & Finite sample properties of OLS estimator	Greene: 1-4	DM:ch-1, 2 Wooldridge( Introductory) Ch 1-4, Appendix- D & E
4	Asymptotic properties of the OLS estimator	Greene: Ch-4	DM: Ch-3, Wooldridge (Introductory) ch-5
5	Hypothesis Testing	Greene: Ch-5	DM: ch-4
6	Regression Modelling: Variable transformations, Functional forms	Greene: Ch 6	Wooldridge( Introductory) ch-6, 7	Test-1 (50%)
7-8	Multicollinearity, Heteroscedasticity and Autocorrelation	Wooldridge: Ch-8 (from introductory Econometrics)	Green Ch-4
9	Model Specification error, Omitted variable bias and Measurement errors	Wooldridge: Ch-4	Wooldridge( Introductory) ch-9
10-11	Instrumental Variable Regression Model	Wooldridge, Ch-15 (from Introductory Econometrics) Wooldridge:5-6	Angrist & Pischke, Ch-4
12	Maximum Likelihood Method	Wooldridge: Ch-13	DM : Ch-10
13-15	Logit/Probit Regression Mode	Wooldridge: Ch-15	J. Scott Long, Ch-3, Gelman Ch 3	Test-2 (50%)

  Note: *This is only a tentative week plan.

7.     Pedagogy:

a.     Instructional strategies: lectures.

b.     Special needs (facilities, requirements in terms of software, studio, lab, clinic, library, classroom/others instructional space; any other – please specify): Classroom equipped with a projector.

c.     Expertise in AUD faculty or outside: Economics faculty at AUD are competent to handle the course.

d.     Linkages with external agencies (e.g., with field-based organizations, hospital; any others NA.

9. Assessment Structur0

Two assessments with 50 percent weightage each or to be decided by the course coordinator in accordance with the university rule.