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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS IKT 6612 ADVANCED ECONOMETRIC APPLICATIONS ELECTIVE 3 0 0 5

Offered By

Economics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR FIRAT GÜNDEM

Offered to

Economics

Course Objective

To internalize models that explain economic theory with the help of advanced econometric analysis techniques and discuss findings of applied literature.

Learning Outcomes of the Course Unit

1   To be able to define models which are parallel to objective functions oriented to explaining economics models while seperating econometric models 2   To be able to internalize and search econometric models that are parallel to developments in economic theory 3   To be able to define data analysis (time series, cross section, pool data, etc.) which are consistent with theories explaining researh subject and get effective estimators directed to policy making 4   To be able to develop forecastings which are parallel to researcher s objective function 5   To be able to discuss criticisms about econometric models and compare evonometric models

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Statistical and Mathematical Foundations 2 Simultaneous equation systems 3 VAR - I 4 VAR - II 5 Panel Data 6 Panel Cointegration - I 7 Panel Cointegration - I I 8 GMM - I 9 GMM - I I 10 Dynamic Panel and ARDL 11 Spatial Econometrics Models 12 Machine Learning Techniques - I 13 Machine Learning Techniques - II 14 Time Series

Recomended or Required Reading

Wooldridge, J. M. (2016). Introductory econometrics: A modern approach. Nelson Education. Greene, W. H. (2018). Econometric analysis. Pearson Education India. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer. Taddy, M. (2018). Modern Bussiness Analytics.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

firat.gundem@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Applying activity 14 3 42 Preparing assignments 1 10 10 Reading 1 10 10 Midterm 1 12 12 Final 1 12 12 Project Final Presentation 1 3 3 TOTAL WORKLOAD (hours) 131

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 LO.1 5 5 5 5 5 5 5 5 5 LO.2 5 LO.3 5 LO.4 5 LO.5 5