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| SBE5301 | Quantitative Research Applications in Social Sciences-II | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Zehra ABDİOĞLU | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The general aim of this course is to enable students to gain knowledge and skills towards quantitative research methods in social sciences. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | learn basic econometric methods. | 1,3 | 1,3,4, | | PO - 2 : | gain the ability to estimate the basic econometrics models. | 1,3 | 1,3,4, | | PO - 3 : | analyze data using the Eviews program. | 1,3 | 1,3,4, | | PO - 4 : | gain the ability to interpret econometric model predictions economically. | 1,3 | 1,3,4, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Models with binary dependent variables (linear probability model, logit, and probit), panel data regression models (pooled OLS, fixed effects model, random effects model), diagnostic tests in panel data regression analysis, robust estimators in panel data regression analysis, autoregressive conditional heteroscedasticity models (ARCH, GARCH, TGARCH, EGARCH), applications of Eviews. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Eviews Program, Data Sources, and Data Sets | | | Week 2 | Models with Binary Dependent Variables (Linear Probability Model) | | | Week 3 | Models with Binary Dependent Variables (Logit Model) | | | Week 4 | Models with Binary Dependent Variables (Probit Model) | | | Week 5 | Panel Data Regression Models (Pooled OLS) | | | Week 6 | Panel Data Regression Models (Fixed Effects Model) | | | Week 7 | Panel Data Regression Models (Random Effects Model) | | | Week 8 | Diagnostic Tests in Panel Data Regression Analysis | | | Week 9 | Mid-term-exam | | | Week 10 | Robust Estimator in Panel Data Regression Analysis | | | Week 11 | Eviews Applications in Panel Data Analysis | | | Week 12 | Quiz | | | Week 13 | Autoregressive Conditional Heteroscedasticity Models (ARCH and GARCH) | | | Week 14 | Asymmetric Autoregressive Conditional Heteroscedasticity Models (TGARCH and EGARCH) | | | Week 15 | Applications of Autoregressive Conditional Heteroscedasticity Models in Eviews | | | Week 16 | End-of-term exam | | | |
| 1 | Gujarati, D. N., Porter, D. C. 2009: Basic Econometrics, McGraw-Hill. | | | |
| 1 | Wooldridge, J. M. 2009: Introductory Econometrics: A Modern Approach, Macmillan Publishing. | | | 2 | Gujarati, D. N. 2011: Econometrics by Example, McGraw-Hill. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 04/2024 | 1 | 30 | | Homework/Assignment/Term-paper | 12 | 05/2024 | 1 | 20 | | End-of-term exam | 16 | 06/2024 | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Arasınav için hazırlık | 10 | 14 | 140 | | Arasınav | 1 | 1 | 1 | | Ödev | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 13 | 1 | 13 | | Total work load | | | 225 |
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