Econometrics of Cross Section Data
- Course
- ECON350 - Econometrics of Cross Section Data
- Department
- Economics - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 6
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
Course Description
The course accentuates primary data collection via surveys and experimental design. Unlike traditional econometrics courses that rely on secondary data, students will develop and execute binary-response surveys, conduct straightforward randomized experiments, and acquire comprehensive skills in sampling, coding, and estimation. The key models discussed include the linear probability model, logit, probit, and fundamental discrete choice models. Practical laboratory sessions are designed to equip students with essential syntax skills for analyzing real-world data. An early introduction to the conceptual distinction between binary and Likert scale formats is provided to establish foundational understanding. The practical emphasis is on binary and experimental survey methodologies.
Econometrics of Cross Section Data
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to Statistical Methodoly |
| Week 2 | Economic Decision models |
| Week 3 | Cross sectional data |
| Week 4 | Simple regression review ; interpretation of Coefficient |
| Week 5 | Introduction to Dataset; Descriptive Statistics in Stata |
| Week 6 | Dummy Variables and Treatment Effects |
| Week 7 | Midterm |
| Week 8 | Linear Probability Model(LPM) |
| Week 9 | Logit model- Intuition |
| Week 10 | Logit Estimation and Interpretation |
| Week 11 | Marginal Effects |
| Week 12 | Model Comparison: LPMvs LOgit |
| Week 13 | Applied Interpretation & policy Relevance |
| Week 14 | Project Guidance and discussion |
| Week 15 | Final; Project submission |
Reference Books & Course Materials
- 01 Jeffery Wooldridge, Introductory Econometrics: A modern Approach-7thEd, 2020.
- 02 Alan Agresti, Categorial Data Analysis, 2013.
Learning Outcomes
- L01 To define quantitative survey meachanisms SOLO 2
- L02 To categorize primary data and secondary data SOLO 4
- L03 To process binary and categorical outcomes SOLO 3
- L04 To illustrate the logic of survey and experimental data, code and interpret binary variables SOLO 2
- L05 To estimate and interpret causal reasoning, with applied skills rather than mathematical derivations SOLO 5
Program Outcomes
- P01 1. Gained the necessary skills to learn and adopt a spirit of continuous learning in their professional life.
- P02 2. Developed various skills by working independently via specific learned techniques that both triggered individual efforts and encouraged collaborative work.
- P03 3. Acquired the essential knowledge and skills to apply key theories, models and applications within the global business context.
- P04 4. Adopted and applied critical and strategic thinking together with research capabilities for efficient and effective business decisions.
- P05 5. Utilized and managed ethically information technologies required for contemporary business world.
- P06 6. Been equipped with conceptual and analytical skills necessary for team work within global organizations.
- P07 7. Displayed their learnt communication skills that are required to effectively interact in a cross cultural and diverse international work environment.
- P08 8. Applied their acquired knowledge within the changing work environment.
- P09 9. Developed an understanding of ethical perspectives and social responsibilities to be used at work and in personal life.
Po-Lo Matrix
| LO | P01 | P02 | P03 | P04 | P05 | P06 | P07 | P08 | P09 | Average |
|---|---|---|---|---|---|---|---|---|---|---|
| L01 | - | - | - | - | - | - | - | - | - | - |
| L02 | - | - | - | - | - | - | - | - | - | - |
| L03 | - | - | - | - | - | - | - | - | - | - |
| L04 | - | - | - | - | - | - | - | - | - | - |
| L05 | - | - | - | - | - | - | - | - | - | - |