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 6065 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 evonometric 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, System Approaches (VAR)
3 Panel Data Concepts, Balanced and Unbalanced Panel Data definitions, differences between panel data, time series and cross sectional data. The concept of heterogeneity, economic meaning and modelling of the unit effect and time effect
4 Panel (pooled) OLS estimation, fixed and random effect panel data models. Fixed an 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: Second generation unit root and cointegration test, unit root with structural breaks and cointegration test
10 Midterm Exam
11 Panel Logit, panel probit and panel tobit models
12 Panel VAR, Panel ARDL and Panel Causality tests
13 Spatial Panel Data Models, spatial weight matrix, 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.
Referances: The publications(SSCI, Econlit vb.) based on economic and econometric methods.

Planned Learning Activities and Teaching Methods

Face-to-face.

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

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 25 25
Preparation for final exam 1 35 35
Reading 2 10 20
Preparing assignments 1 20 20
Midterm 1 3 3
Final 1 4 4
TOTAL WORKLOAD (hours) 191

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.1555555555
LO.25
LO.35
LO.45
LO.55

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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