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| EKO4006 | Computer Applications in Econometrics-II | 3+0+0 | ECTS:6 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of ECONOMETRICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Lab work | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Zehra ABDİOĞLU | | Co-Lecturer | DOCTOR LECTURER Havvanur Feyza ERDEM, | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This is continuation of Applied Econometrics-I course and objectives of the course are the same as objectives of the Applied Econometrics-I. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | learn what are the econometrics tools | 1,2,3,4,5,6,7 | 1,3,4 | | LO - 2 : | learn how to do econometric tools use | 1,2,3,4,5,6,7 | 1,3,4 | | LO - 3 : | learn how to do econometric tools apply to economic problems | 1,2,3,4,5,6,7 | 1,3,4 | | LO - 4 : | learn how to do economic problems analysis by using econometric tools | 1,2,3,4,5,6,7 | 1,3,4 | | LO - 5 : | learn how to find as solution find out to economic problems by using econometric tools | 1,2,3,4,5,6,7 | 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), LO : Learning Outcome | | |
| Autocorrelation: OLS Estimation in the presence of autocorrelation, consequences of using OLS in the presence of autocorrelation, detecting autocorrelation, remedial measures, autoregressive conditional heteroscedasticity model, Types of specification errors, consequences of specification errors, tests of specification errors, errors of measurement, Regression on dummy variables: Comparing two regressions (the dummy variable approach) , piecewise linear regression, the use of dummy variables in combining time series and cross sectional data, Dummy dependent variable: the linear probability model (LPM) , problems in estimation of LPM, the logit model, the probit model, the tobit modelAutoregressive and distributed lag models: estimation of distributed lag models, the Koyck approach to distributed lag models, estimation of autoregressive models, the method of instrumental variables, detecting autocorrelation in autoregressive models, the Almon approach to distributed lag models, causality in economics: the Granger testSimultaneous-equation models: examples of simultaneous-equation models, the simultaneous-equation bias, a test of simultaneity, tests for exogeneity, Approaches to estimation: recursive models and ordinary least squares, estimation of a just identified equation (the method of indirect least squares) , estimation of an overidentified equation (the method of two-stage least squares) , |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Autocorrelation problem | | | Week 2 | Detection autocorrelation | | | Week 3 | Eliminating autocorrelation | | | Week 4 | Dummy variables | | | Week 5 | Stepwise regressions | | | Week 6 | Piecewise regressions | | | Week 7 | Logit models | | | Week 8 | Probit models | | | Week 9 | Mid-term exam | | | Week 10 | Tobit models | | | Week 11 | Quiz | | | Week 12 | Lag distributed models | | | Week 13 | Koyck and Almon models | | | Week 14 | System equations | | | Week 15 | Simultaneous equations systems | | | Week 16 | End-of-term exam | | | |
| 1 | Yamak, R. ve Köseoğlu, M. 2006; Uygulamalı İstatistik ve Ekonometri, Aksakal Yayınları, Trabzon. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | 04/2017 | 1 | 30 | | Homework/Assignment/Term-paper | 12 | 04/2017 | 2 | 20 | | End-of-term exam | 16 | 06/2017 | 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 | 2 | 15 | 30 | | Sınıf dışı çalışma | 7 | 10 | 70 | | Laboratuar çalışması | 4 | 11 | 44 | | Arasınav için hazırlık | 2 | 2 | 4 | | Arasınav | 1 | 1 | 1 | | Uygulama | 2 | 10 | 20 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 5 | 2 | 10 | | Dönem sonu sınavı | 1 | 1 | 1 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 180 |
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