Econometric Analysis

By Sugata Sen Roy | University of Calcutta

Learners enrolled: 649

The course is intended for postgraduate students in statistics, economics and related social sciences. It is meant to help students to model and analyze data arising from experimentations in economic and social sciences. A basic level of mathematics is required. However, a student must have some general idea of descriptive, probabilistic and inferential statistics to fully grasp the nuances of the course.

Summary

Course Status :	Completed
Course Type :	Core
Duration :	15 weeks
Category :	Mathematics
Credit Points :	4
Level :	Postgraduate
Start Date :	13 Jan 2020
End Date :	30 Apr 2020
Enrollment Ends :	28 Feb 2020
Exam Date :	10 May 2020 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week-1

0 Introduction to Econometrics

1 Introduction to multiple regression I

2 Introduction to multiple regression II

3 Box-Cox transformation

Week-2

4 Regression with dummy variables I

5 Regression with dummy variables II

6 Piece-wise regression – Spline functions

Week-3

7 Detecting outliers using R

8 Detection of Outliers- Robust techniques

9 Detection of Outliers- Part II

Week-4

10 Variables selection techniques I

11 Variables selection techniques II

12 OLS vs GLS

Week-5

13 Consequences of heteroscedasticity

14 Detection of heteroscedasticity

15 Remedial measures in the presence of heteroscedasticity

Week-6

16 Autocorrelation in regression I

17 Autocorrelation in regression II

18 Remedial measures in the presence of autocorrelation

Week-7

19 Consequences of multicollinearity

20 Multicollinearity- Detection

21 Multicollinearity - Remedies I

Week-8

MID-TERM ASSESSMENT

Week-9

22 Multicollinearity - Remedies II

23 Tobit model

24 Tobit model using R

Week-10

25 Errors-in-variables

26 Estimation for Errors-in-variables models 1

27 Estimation for Errors-in-variables models 2

Week-11

28 Checking non-normality

29 Lagged Variables 1

30 Lagged Variables 2

Week-12

31 Simultaneous Equations Model-An Introduction

32 Simultaneous Equations Model-The Identification Problem

33 Simultaneous Equations Model-The Identification Problem (contd.)

Week-13

34 Simultaneous Equations Model-Identification_A Different Approach

35 Simultaneous Equations Model-The Indirect Least Squares Estimation

36 Simultaneous Equations Model-The Two Stage Least Squares Method

Week-14

37 Simultaneous Equations Models-The LIML and LVR methods of Estimation

38 Simultaneous Equations Models-The System Estimation Methods

39 The Seemingly Uncorrelated Regression Models

Week-15

FINAL ASSESSMENT

40 Conclusion

Books and references

J. Johnston : Econometric Methods

G.G. Judge, et.al. : The Theory and Practice of Econometrics (2nded.)

W. Greene : Econometric Analysis

E. Malinvaud : Statistical Methods in Econometrics

D. Gujrati : Basic Econometrics

M.D. Intrilligator, et.al. : Eco. Models, Techniques and Applications

Instructor bio

Sugata Sen Roy

University of Calcutta

I had been teaching at the postgraduate level for the last 30 years. My research and teaching interests have been regression analysis, time series analysis, econometrics, development statistics, multivariate analysis and survival analysis. I have had several publications in these areas and have also guided Ph.D. students in related areas. However, my primary focus has been on teaching statistics and particularly in dissipating statistical knowledge to students of other disciplines.