Rapor Tarihi: 27.03.2026 02:58
| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Econometrics II | ECON302 | Turkish | Compulsory | 6. Semester | 3 + 0 | 3.0 | 5.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | Face to face. |
| Course Coordinator | Doç. Dr. Ömer LİMANLI |
| Instructor(s) | |
| Goals | The aim of this course is to build upon the fundamental econometric methods covered in Econometrics I and address the more complex data structures and estimation problems frequently encountered in econometric applications. Students will learn time series analysis by incorporating key topics such as dynamic relationships, autocorrelation, and heteroskedasticity. The course will teach how to control for unobserved heterogeneity using panel data methods. Students will learn to address endogeneity issues using instrumental variables estimation and the two-stage least squares method, and will understand the rationale behind the specification and estimation of simultaneous equation models. The course will also cover limited dependent variable models, including binary, censored, and discrete dependent variable models, along with sample selection corrections. By the end of the course, students will be able to select appropriate econometric techniques for various types of research, apply these methods using statistical software, critically evaluate empirical studies in the field of economics, and conduct independent quantitative research for their senior theses or future graduate studies. |
| Course Content | The course content consists of time series analysis and panel data methods, as well as models with a limited number of dependent variables. |
| # | Öğrenme Kazanımı |
| 1 | Can build regression models using time series data; can integrate the concepts of stationarity, trend, and seasonality into the modeling process. |
| 2 | Identify serial correlation and heteroskedasticity issues in time series regressions; select and apply appropriate correction methods. |
| 3 | Distinguish between pooled cross-sectional and panel data methods (fixed effects, random effects); select an appropriate estimator based on the data structure. |
| 4 | Can identify the endogeneity problem; can obtain consistent estimates by applying instrumental variable estimation and two-stage least squares (2SLS). |
| 5 | Can analyze the specification problem in simultaneous equation models; can determine appropriate estimation strategies. |
| 6 | Estimate limited dependent variable models (Probit, Logit, Tobit); identify and apply correction methods for sample selection bias. |
| 7 | Understand advanced time series topics (unit root tests, cointegration, VAR models) and evaluate their applicability in empirical research. |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | Basic Regression Analysis with Time Series | Preparation, After Class Study |
| 2. Week | Basic Regression Analysis with Time Series (Continued) | Preparation, After Class Study |
| 3. Week | Serial Correlation and Heteroskedasticity in Time Series Regression | Preparation, After Class Study |
| 4. Week | Serial Correlation and Heteroskedasticity in Time Series Regression (Continued) | Preparation, After Class Study |
| 5. Week | Pooling Cross-Sections Over Time: Simple Panel Data Methods | Preparation, After Class Study |
| 6. Week | Pooling Cross-Sections Over Time: Simple Panel Data Methods (Continued) | Preparation, After Class Study |
| 7. Week | Advanced Panel Data Methods | Preparation, After Class Study |
| 8. Week | Instrumental Variables Estimation and Two-Stage Least Squares | Preparation, After Class Study |
| 9. Week | Instrumental Variables Estimation and Two-Stage Least Squares (Continued) | Preparation, After Class Study |
| 10. Week | Simultaneous Equation Models | Preparation, After Class Study |
| 11. Week | Models with Limited Dependent Variables and Sampling Selection Correction | Preparation, After Class Study |
| 12. Week | Models with Limited Dependent Variables and Sampling Selection Correction (Continued) | Preparation, After Class Study |
| 13. Week | Advanced Time Series Topics | Preparation, After Class Study |
| 14. Week | Advanced Time Series Topics (Continued) | Preparation, After Class Study |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Know the basic concepts of economics at an advanced level; can abstract the economic decision-making units in households, firms, government agencies, and predict the results of their decision-making mechanisms in different market and competition conditions. | ✔ | |||||
| 2 | Understand the objectives of economies such as growth, employment, productivity, sustainability; may propose solutions to problems such as unemployment, contraction, inflation, high interest rates and similar problems. | ✔ | |||||
| 3 | Capable to make sense of the international interactions of national economies with an international economic perspective. | ✔ | |||||
| 4 | Have knowledge about the historical development of the world economy and its current structure, understand the working process of different economic systems. | ✔ | |||||
| 5 | Can combine economic theory with mathematical, statistical and econometric skills to analyze concrete situations; are experienced in economic data collection and analysis methods by utilyzing technology. | ✔ | |||||
| 6 | Analyze the different sectors of the economy such as agriculture, industry and services through the Production Factors such as Labor, Capital, Natural Resource, Information and Entrepreneurship and evaluate the country economies. | ✔ | |||||
| 7 | Know the current status, historical background, the strengths and weaknesses of Turkish economy in macro and micro scale; can propose solutions, make theoretical and applied studies in this direction as individuals and teams. | ✔ | |||||
| 8 | Know the relations of economics as a social science with other sciences especially with politics, business administration, law, history, mathematics and engineering; reflect the theoretical knowledge in practice and have collaborative working culture. | ✔ | |||||
| 9 | Have the consciousness of lifelong learning, use the education to keep knowledge updated and contribute to the business life. | ✔ | |||||
| 10 | Have the cultural consciousness, know their own history, develop awareness of working with different cultures, respect individual, social and cultural rights. | ✔ | |||||
| 11 | Behave in accordance with ethical values in their works, have the ability of questioning, free thinking with no prejudices. | ✔ | |||||
| 12 | Have the consciousness of fulfilling the given tasks at the right time. In this process, utilize in maximum written, visual, audio sources and information-communication technologies. | ✔ | |||||
| 13 | Have the experience and ability to explain what they learn in written, oral and visual manner. | ✔ | |||||
| 14 | Use the Turkish and English language properly and effectively, and pursue professional readings in double language. | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 | DK7 |
|---|---|---|---|---|---|---|---|
| PY1 | 2 | 1 | 2 | 3 | 3 | 2 | 2 |
| PY2 | 1 | 1 | 3 | 2 | 2 | 1 | 3 |
| PY3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| PY4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| PY5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| PY6 | 1 | 1 | 3 | 2 | 2 | 1 | 2 |
| PY7 | 1 | 1 | 2 | 2 | 2 | 2 | 2 |
| PY8 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| PY9 | 2 | 2 | 2 | 2 | 2 | 2 | 3 |
| PY10 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| PY11 | 2 | 3 | 3 | 3 | 3 | 2 | 3 |
| PY12 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| PY13 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| PY14 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 3 | 42 |
|
Ders Dışı |
Preparation, After Class Study | 14 | 3 | 42 |
| Research | 13 | 3 | 39 | |
| Interview | 1 | 2 | 2 | |
|
Sınavlar |
Midterm | 1 | 1 | 1 |
| Final | 1 | 1.5 | 1.5 | |
| Total Workload | 127.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 5.0 | ||
