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| EKO4001 | Computer Applications in Econometrics-I | 3+0+0 | ECTS:8 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of ECONOMETRICS | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Arş. Gör. Serkan SAMUT | | Co-Lecturer | PROF. DR. ZEHRA ABDİOĞLU | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course is complementary of Introduction to Econometrics and Econometric Theory courses. Main objective of this course is to make econometric analyses by using Eviews. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | learn what econometrics tools are | 1,3 | 1, | | LO - 2 : | learn how to use econometric tools | 1,3 | 1, | | LO - 3 : | learn how to apply econometric tools to economic problems | 1,3 | 1, | | LO - 4 : | learn how to analyze economic problems by utilizing from econometric tools | 1,3 | 1, | | LO - 5 : | learn how to solve economic problems by utilizing from econometric tools | 1,3 | 1, | | 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), LO : Learning Outcome | | |
| Fundamentals of Eviews. Estimation of two variable regression model: the method of ordinary least squares (OLS) and the method of maximum likelihood (ML). Interval estimation and hypothesis testing: confidence intervals for regression coefficients and error variance, hypothesis testing via confidence interval and the test of significance approaches, Regression analysis and analysis of variance. Regression through the origin, scaling and units of measurement. Functional form of regression models: log-log, log-lin, lin-log and reciprocal models. Hypothesis testing in multiple regression: hypothesis testing about individual partial regression coefficients, testing the overall significance of the sample regression, testing the equality of two regression coefficients, testing linear equality restrictions (the t test approach and the f test approach), testing for structural stability of regression models, testing the functional form of regression. Prediction with multiple regression. Multicollinearity: estimation in the presence of perfect multicollinearity, estimation in the presence of high but imperfect multicollinearity, consequences of multicollinearity, detection of multicollinearity, remedial measures. Heteroscedasticity: OLS estimation in the presence of heteroscedasticity, the method of generalized least squares, consequences of using OLS in the presence of heteroscedasticity, detection of heteroscedasticity, remedial measures. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basics of E-views and R | | | Week 2 | Estimation of simple resgression, Ordinary Least Squares (OLS) | | | Week 3 | Determination coefficient, t-test, F-test | | | Week 4 | Interval estimation and Hypothesis testing | | | Week 5 | Regression analysis and variance analysis | | | Week 6 | Functional form of regression equation, MWD test, RESET test | | | Week 7 | Functional form of regression equation, log-log, lin-log, log-lin and reciprocal models | | | Week 8 | t and F test in multivariate regressions | | | Week 9 | Mid-term exam | | | Week 10 | Structural stability test | | | Week 11 | An Application | | | Week 12 | Multicolinearity problem | | | Week 13 | Detection and eliminatibg multicolinearity problem | | | Week 14 | Heteroscedasticity problem, detection of heteroscedasticity | | | Week 15 | Eliminating the heteroscedascity problem | | | Week 16 | End-of-term exam | | | |
| 1 | Yamak, R. ve Köseoğlu, M. 2015, Uygulamalı İstatistik ve Ekonometri, Aksakal Yayınları, Trabzon. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 11/11/2023 | 1 | 50 | | End-of-term exam | 16 | 26/01/2024 | 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 | 5 | 14 | 70 | | Laboratuar çalışması | 4 | 14 | 56 | | Arasınav için hazırlık | 24 | 1 | 24 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 46 | 1 | 46 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 240 |
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