COURSE UNIT TITLE

: APPLIED ECONOMETRICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
FIB 5117 APPLIED ECONOMETRICS ELECTIVE 3 0 0 6

Offered By

Financial Economics and Banking

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR HAKAN KAHYAOĞLU

Offered to

Financial Economics and Banking

Course Objective

The aim of the course is to test economic (micro and macro) models that are subject to economic theory with the help of econometric analysis techniques, to use artificial intelligence tools with approaches that will provide the basis of data science for economists (machine learning, deep learning) and to provide the student with the ability to gain confidence in economic and financial forecasting.

Learning Outcomes of the Course Unit

1   To be able to distinguish econometric models
2   To be able to internalize and to explain the mathematical statistical techniques which is reference for econometric models in practice.
3   To be able to do hypothesis tests and to obtain parametrical predictors intended for economic models.
4   To be able to make prediction through parametrical predictors that were obtained from econometric models.
5   To be able to test the critics directed to econometric models through primary and secondary datas.
6   To be able to use machine learning, deep learning approaches and artificial intelligence tools as analysis tools

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 The effects of technological developments on econometrics and economics
2 Bivariate regression analysis and hypothesis tests
3 Multivariate time series analysis and interpretations
4 Basic econometric problems in multivariate analysis
5 Introduction to open access coded tools: R, Phyton, Julia
6 Practices about R, Phyton and Julia
7 Qualititive dependent variable models, practices of R, Phyton and Julia
8 Midterm
9 Dynamic econometric modelling, practices of R, Phyton and Julia
10 Simultaneous equations models,practices of R, Phyton and Julia
11 Introduction to time series analysis
12 Fundamental time series analysis (Unit root, cointegration and breaks)
13 Multivariate time series analysis (VAR and advanced time series)
14 Machine and deep learning, artificial intelligence in econometrics

Recomended or Required Reading

Main Sources / Assistant Sources:
-Gujarati, Damodar N. (2016), Örneklerle Ekonometri, BB101 Yayınları
-Chris, Brooks. (2014), Introductory Econometrics for Finance, 3rd edition by Palgrave Cambridge University Press
-R. Carter Hill, William E. Griffith and Guay C. Lim., (2007), Principles of Econometrics by John Wiley & Sons
-Dimitrios Asteiou and Stephan Hall (2015), Applied Econometrics 3rd edition by Palgrave
-Enders, Walter (1995), Applied Econometric Time Series, John Wiley and Sons, USA.
- Uygur, Ercan (2001), Ekonometri: Yöntem ve Uygulama, Imaj Yay., Ankara.
-James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning with Applications in R, (2021)
-James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning with Applications in Python (2023)
-Referanslar: Iktisadi ve ekonometrik modelleme Temelli (SSCI, Econlit vb.) yayınlar.
-Other Matreials, Software for econometrics and data analysis
Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer, Introduction To Econometrics With R,https://www.econometrics-with-r.org/, (2025-07-23)
Florian Heiss Using R for Introductory Econometrics, 2nd edition, 2020, http://www.urfie.net/
Florian Heiss, Daniel Brunner, Using Python for Introductory Econometrics, 2020, http://www.urfie.net/
-Han, Jiawei (2011), Data Mining: Concepts and Techniques, 3rd
Open Resources
R, Python, Julia

Planned Learning Activities and Teaching Methods

As well as lecture and discussing about the facts, every subject will be supported by econometric applications suitable with the theoritical framework which is defined.

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 * 0.20 + STT * 0.30 + FIN* 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.30 + RST* 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Presentations and written documents that include the learning objects mentioned above, verbal discussions during the lesson, and the evaluation of discussions about the applicable results within the group.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

hakan.kahyaoglu@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lecture 14 3 42
Weekly pre-class activities 14 2 28
Preparation to midterm 1 10 10
Preparation to final exam 1 15 15
Other 1 25 25
Preparing assignments 2 9 18
Mid Term 1 3 3
Final Exam 1 4 4
TOTAL WORKLOAD (hours) 145

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9
LO.135
LO.23445
LO.3443
LO.442
LO.5155
LO.6

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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