Faculty of Business and Management Sciences · Business Administration (English 30%) · Undergraduate
Course Objective
The aim of this course is to equip students with basic econometric methods. Econometrics is a social science that analyzes economic data under the light of economic theory. Since the nature of economic data does not permit straightforward adaptation of statistical methods special estimation and inference methods have been developed. Introduction to Econometrics teaches these methods in the cross-sectional data framework. Applications will be carried out in EViews and GRETL.
Course Content
Simple regression model, OLS estimation, classical regression model, statistical inference, t, F and LM tests, finite sample and asymptotic properties, dummy variables, specification error and heteroskedasticity
Required Resources
[W] Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, Cengage
Recommended Resources
Arnold H. Studenmund, Using Econometrics a Practical Guide, Pearson.
Course Learning Outcomes
- Develop knowledge of applied econometrics
- Understand the assumptions upon which different econometric methods are based and their implications
- Use statistical software to implement the various techniques taught in the course
- Interpret and critically evaluate applied work and econometric findings
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 | 3 | 48 |
| Midterm | 1 | 10 | 10 |
| Quiz | 2 | 2 | 4 |
| Assignment | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Final | 1 | 15 | 15 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Simple regression model | Wooldridge (Bölüm 1, Appendix A, B, C) |
| 2 | OLS estimation method | Wooldridge (Ch.2) |
| 3 | Assumptions of CLRM | Wooldridge(Ch. 3) |
| 4 | OLS estimation of multiple linear regression model, properties of OLS estimators | Wooldridge(Ch. 3) |
| 5 | Finite sample properties of OLS estimators, Unbiasedness and efficiency, Gauss-Markov Theorem | Wooldridge(Ch.3 ) |
| 6 | Hypothesis testing: t-test, interval estimation | Wooldridge(Ch. 4) |
| 7 | Hypothesis testing: F-test, testing linear restrictions | Wooldridge(Ch. 4 ) |
| 8 | Ara Sınav | Ara Sınav |
| 9 | Asymptotic properties of OLS estimators, consistency, asymptotic efficiency, asymptotic normality, LM test | Wooldridge(Ch. 5) |
| 10 | Functional form, goodness of fit measures, forecasting and residual analysis | Wooldridge(Ch. 6 ) |
| 11 | Dummy variables | Wooldridge(Ch. 7) |
| 12 | Heteroskedasticity | Wooldridge(Ch. 8) |
| 13 | Heteroskedasticity | Wooldridge(Ch. 8) |
| 14 | Functional form misspecification, measurement errors | Wooldridge(Ch. 9) |
| 15 | Proxy variables, Instrumental Variables | Wooldridge(Ch. 9) |
| 16 | Final Sınavı | Final Sınavı |