Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
1	DSBE514	PANEL DATA ECONOMETRICS	3+0+0	3	7,5	13.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	DATA SCIENCE
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Introducing panel data, its types, common usage areas and main characteristics. Introducing main static panel data estimation methods with their statistical and econometric foundations. Showing the use of panel data analysis techniques including addressing of specific research questions.
Course Content	Main aim of the course is to cover panel data analysis techniques and their applications in economics, business and related fields. Static panel data models: pooled OLS, first differences, fixed effects, random effects, LSDV, IV, 2SLS and GMM are examined throughout the course. Models are introduced encompassing solid theoretical foundations and standard examples. By the end of the course students will have completed multiple assignments - including data analysis and interpretations of results.- and have practiced applications of these models with scientific rigor and structure.
Course Methods and Techniques	Traditional in class lectures and real life applications and cases will be utilized.
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Instructor Dr. Seyit GÖKMEN seyit.gokmen@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	-
Course Notes	Course materials will be provided throughout the semester

Course Category

Mathematics and Basic Sciences	%20
Social Sciences	%40
Field	%40

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Araştırma Ödevi	2	20	40
Yazılı Sınav	1	30	30
Sunum için Hazırlık	1	20	20
Sunum	1	20	20
Proje	1	15	15
Rapor	1	20	20
Yazılım Deneyimi	1	20	20
Yüz Yüze Ders	14	3	42
Final Sınavı	1	20	20
Total Work Load			Number of ECTS Credits 7,5 227

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Describe and characterize panel data
2	Conduct exploratory data analysis with panel data
3	Apply common static panel data estimation techniques
4	Analyze panel data estimation results
5	Identify the necessity and usage areas of panel data

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to course and structure of panel data	Review syllabus and example data structure	Syllabus
2	Types of panel data: fixed and random effects	Compare fixed vs. random effects	Lecture notes
3	Estimation: Pooled OLS, Fixed Effects, Random Effects	Study assumptions of each model	Textbook chapter
4	Model selection: Hausman test	Prepare Hausman test application	Application guide
5	Time-fixed effects models and dummy variables	Build a time-fixed effect model	Application file
6	Heteroskedasticity and autocorrelation issues	Study error structure in panel data	Reading material
7	Introduction to dynamic panel models	Read about Arellano-Bond method	Article
8	Midterm Exam	Review previous topics	-
9	Arellano-Bond and GMM estimators	Build basic example using GMM	Lecture notes
10	Endogeneity issue and solutions	Study instrument selection	Case study
11	Violations of assumptions in panel regressions	Revise test techniques	Application file
12	Panel cointegration and long-run relationships	Research panel unit root and cointegration	Academic article
13	Student presentations and empirical applications	Finalize and rehearse your presentation	Presentation files
14	Final exam preparation and wrap-up	General revision for the final exam	Course summary

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=76785&lang=en