Syllabus

Course description

This course is designed to provide a foundation of econometric analysis, with an emphasis on
economic applications. Topics include estimation and statistical inference in regression models.
We begin with the standard regression model, and derive the properties of the estimators–
validity and efficiency. We will then relax assumptions one by one and explore how these
properties can be restored.

Course objectives

The ultimate goal is actual applications of econometric methods using the real data and

economic models, and therefore this course requires understanding of econometric software and

computing skills. For the empirical work, econometric computer software will be studied. The

recommended statistical software is Eviews. The teaching assistant (TA) will guide and assist

Assignments

Class assignments will be passed out every three weeks approximately. These assignments will

include both problem solving and empirical tasks. The assignments will be graded by the

teaching assistant, and will be reviewed in the class. Class attendance and participation will be

Team project will be given for each group of five students. The goal of the project is to obtain
empirical assessment of economic models or any interesting subjects such as the consumption
function, Okun’s law, Taylor rule, MLB players’ salary determination, and forecasting the
gasoline price. Evaluation will be based on presentation and the term paper.

Required Textbook

Jeffrey Wooldridge, Introductory Econometrics, 4th ed., Thomson South-Western

Optional References

Paul Newbold, Statistics for Business and Economics, 6th ed., Prentice Hall.
Berndt, Practice of Econometrics, Addison-Wesley.
남준우·이한식, 계량경제학.

Grading Evaluation

Assignments: 20%. Midterm Exam: 30%. Final Exam: 30%.
Term Paper: 10%. Attendance: 10%.