Faculty of Business and Management Sciences · Business Administration (English 30%) · Undergraduate
Course Objective
Econometrics II builds upon the foundation established in Econometrics I and covers more advanced econometric techniques and their applications. This course comprises essential topics, including model specification and functional form selection, multicollinearity, serial correlation, heteroskedasticity, time-series models, dummy dependent variable techniques, and simultaneous equations. Students will learn how to diagnose and address common econometric problems, apply appropriate estimation methods, and interpret the results of their analyses. The course emphasizes both theoretical understanding and practical application through project work.
Course Content
This course covers model specification and choice of functional form, multicollinearity, serial correlation, heteroskedasticity, time series models, dummy dependent variable techniques, and simultaneous equations.
Required Resources
Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 2nd ed., Thomson Learning, 2020
Startz, R. (2009). Eviews illustrated for version 9. Quantitative Micro Software.
Recommended Resources
Joshua D. Angrist, & Pischke Jörn-Steffen, Mostly Harmless Econometrics: An Empiricist's Companion, Princeton University Press, 2009.
Rules
This course follows IZU's attendance policies. The University says that you can have four unexcused absences, but the University does not really differentiate between excused and unexcused absences. Weddings, funerals, illnesses (cold, flu, vertigos etc.), missing the shuttle in the morning and having a regular job in or out of the campus are not excused absences. In fact, the lecturer will not accept any excuse for being an absentee. Do not bring any medical reports for your illness. It will not be accepted as an excuse. So plan accordingly. If you have more than four absences, you will fail the course. In this case, you will not be allowed to take the final exam. Your letter grade will be DZ.
The lecturer will take attendance at the beginning of each lecture and will upload it to the KAMPUS system directly. Please do not forget that coming to class more than ten minutes late will be considered an absence, but please come to class even if you're going to be marked absent because the information you learn in the class is important. If you leave before the end of a lecture, you will be marked absent for this lecture.
The easiest solution to all of this, of course, is just not to miss lectures.
Course Learning Outcomes
- Knows how to select appropriate functional forms in model setup
- Detect serial correlation in time series data and take it into account in model setup
- Can detect and correct heteroskedasticity in regression models
- Can detect and correct multicollinearity in regression models
- Can set up dummy variable models
- Can analyze time series data
- Be able to apply simultaneous equation modeling techniques
- Develop skills to conduct independent econometric research and present findings effectively
- Gain practical experience using econometric software to analyze real-world data
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 16 | 5 | 80 |
| Midterm | 1 | 2 | 2 |
| Quiz | 0 | 0 | 0 |
| Assignment | 0 | 0 | 0 |
| Practice | 1 | 1 | 1 |
| Final | 1 | 2 | 2 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to the course and to the software | W. (Ch. 10) |
| 2 | Basic regression analysis with time-series data | W. (Ch.10) |
| 3 | Basic regression analysis with time-series data | W. (Ch.10) |
| 4 | Further issues in using OLS with time-series data | W. (Ch.11) |
| 5 | Quiz 1 and EViews Application | |
| 6 | Further issues in using OLS with time-series data | W. (Ch.11) |
| 7 | EViews Application | |
| 8 | Midterm | |
| 9 | Serial Correlation in Time Series Regression | W. (Ch.12) |
| 10 | Serial Correlation in Time Series Regression | W. (Ch.12) |
| 11 | Pooling cross section across time | W. (Ch.13) |
| 12 | Quiz 2 and EViews Application | |
| 13 | Advanced time series topics | W. (Ch.18) |
| 14 | Advanced time series topics | W. (Ch.18) |
| 15 | EViews Application | |
| 16 | Final |