COURSE UNIT TITLE

: ADVANCED ECONOMETRIC APPLICATIONS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IKT 6055 ADVANCED ECONOMETRIC APPLICATIONS ELECTIVE 3 0 0 8

Offered By

Economics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR RECEP KÖK

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 econometric models.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Univariate and bivariate micro and macro econometric models
2 Mathematical concepts: Simultaneous Equations, Systems Approach (VAR)
3 Panel Data Concepts, Balanced and Unbalanced Panel Data definitions. Panel Data, the differences between time-series and cross sectional data. The concept of heterogeneity, Economic meaning of the unit and time effects and modeling.
4 Panel (pooled) OLS estimation, fixed and random effect panel data dodels. Fixed and Random Effect Tests (Hausman test)
5 Heteroscedasticity and autocorrelation tests
6 Dynamic Panel Data Models and GMM
7 Cross Section Dependence and Heterogeneity Test in Panel Data Analysis
8 Nonstationary Panel Data Analysis: First Generation Unit Root and Cointegration Test
9 Nonstationary Panel Data Analysis: The Second Generation Unit Root and Cointegration Test, Unit Root with Structural Breaks and Cointegration Test
10 Midterm Exam
11 Panel logit, probit panel and the panel Tobit models
12 Panel VAR, Panel ARDL and Panel causality tests
13 Spatial Panel Data Models, Spatial Weight Matrix, the spatial dependency test, Hausman Test
14 SAR, SEM, SDM, SARAR Models, Spatial Dynamic Panel Data Analysis. ML GMM, Two Stage EKK Estimators

Recomended or Required Reading

Kök, R. & E. Deliktaş (2003), Endüstri Iktisadında Verimlilik Ölçme ve Strateji Geliştirme Teknikleri, Izmir.
Christian Gourierous,Joann Jasiak,Financial Econometrics: Problems, Models, and Methods, Princeton Series in Finance,2001.
Chris Brooks, Introductory Econometrics for Finance, Cambridge University Press, 2008.
Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, Sergio M. Focardi Teo Ja i , Financial Econometrics: From Basics to Advanced Modeling Techniques,2007.
Carol Alexander, Practical Financial Econometrics,2008
Carol Alexander, Quantitative Methods in Finance,2008
Carol Alexander, Value at Risk Models,2008.
Terence C. Mills ve Raphael N. Markellos, The Econometric Modelling of Financial Time Series,2008.

Planned Learning Activities and Teaching Methods

Expression, as well as classroom lessons and discussions about the cases, in accordance with the theoretical framework will be supported by each topic in computer applications in econometrics

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 * 0.25 + STT * 0.25 + FIN* 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE* 0.25 + STT * 0.25 + RST* 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

recep.kok@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 20 20
Preparation for final exam 1 30 30
Preparing assignments 2 15 30
Reading 2 15 30
Final 1 4 4
Midterm 1 3 3
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.155555
LO.255
LO.3555555
LO.455
LO.5555

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