Introduction to Econometrics
Lecturer: Hieu Nguyen
Dates: Fall semester 2024
Lectures, Seminars: Friday, 9:00 – 11:50 (VT105)
Office hours: Friday, 12:00 – 14:00 (email in advance)
Course Description
The course is designed to give students experience of using basic econometric methods important in
economics and other business subjects. It provides skills in regression essential for understanding
much of the literature of economics, finance, and empirical studies in other areas of business.
The main textbook:
• Wooldridge, J.M. Introductory Econometrics – A Modern Approach. 5th ed. Michigan State
University, 2013. ISBN-13: 978-1-111-53104-1.
• Studenmund, A. H. (2011): Using Econometrics: A Practical Guide. 6th Edition, Pearson
Addison Wesley. ISBN: 978-0-13-136773-9.
• Using Gretl for principles of Econometrics, available at link
https://www.learneconometrics.com/gretl/index.html.
Pre-requisites
Basic matrix algebra, elementary probability and mathematical statistics.
Course objectives
The course is designed to give students experience of using econometric methods important in
economics, finance and other business subjects. It provides skills in regression essential for
understanding much of the literature of economics, finance, and empirical studies in other areas of
business.
We begin with the simple regression and multiple regression models. They are treated in depth and in
range of applications. Careful attention is given to the interpretations of regression results and
hypothesis testing.
By the end of the course, students should be able to use regression models in many different
applications and to critically examine reported regression results in empirical research in economics
and other business studies. They will be able to identify and deal with several econometric problems
in the analysis of cross-section data and will have experience of a range of basic econometric methods.
The course is designed to give students an understanding of why econometrics is necessary and to
provide them with a working knowledge of basic econometric tools so that: they can apply these tools
to modelling, estimation, inference, and forecasting in the context of real-world economic problems;
they can evaluate critically the results and conclusions from others who use basic econometric tools;
they have a foundation and understanding for further study of econometrics and they have an
appreciation of the range of more advanced techniques that exist and that may be covered in later
econometric courses.
Grading (Tentative)
Quick Quizzes 10%
There are 2 in-class quizzes, each makes for 5%. Students will be announced a week in advance.
Home assignment: 20 %
There will be two home assignments that accounts for 10 points each. These exercises will enhance
your problem-solving skills and prepares you for exams.
Midterm exam: 30 %
The midterm exam will take place during regular seminar time and will be informed at least 1 week in
advance. You are not allowed to use any textbook, any notes or electronic devices. There will be no
make-up or alternative dates for the midterm exam.
Final Exam: 30 %
Final exam will take place on will be informed at least 1 week in advance. Students are highly
encouraged to take the final exam during the first offered slot since grades are adjusted based on
average class performance. There are no such adjustments for the exams taken during the alternative
date.
Grade distribution:
A: 85 – 100
B: 70 - 84
C: 60 – 69
D: 50 – 59
F: 0 – 49
Outline
(This is a tentative schedule to be updated based on students' progress.)
1. Introduction to econometrics and working with data
2. OLS – introduction
3. OLS – assumptions
4. OLS inference – hypothesis testing
5. OLS inference – multiple hypothesis testing
6. Non-linear specification and dummy variables
7. Omitted and irrelevant variables
8. Multicollinearity and heteroskedasticity
9. Autocorrelation (Serial correlation)
10. Endogeneity
11. Binary dependent variable
12. (if time allows) Instrumental variables method