ECON 301 | Course Introduction and Application Information - Department of Mathematics

Course Name

Econometrics

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
ECON 301	Fall/Spring	3	0	3	6

Prerequisites

	ECON 101 To succeed (To get a grade of at least DD)
and	ECON 102 To succeed (To get a grade of at least DD)

Course Language

English

Course Type

Elective

Course Level

First Cycle

Mode of Delivery

-

Teaching Methods and Techniques of the Course

-

National Occupation Classification

-

Course Coordinator

Doç. Dr. Gül ERTAN ÖZGÜZER

Course Lecturer(s)

Prof. Dr. Celal KÜÇÜKER

Assistant(s)

Araş. Gör. Samet ŞİMŞEK

Course Objectives	The main objective of the course is to improve the students’ basic statistical knowledge to conduct advanced level econometric analysis. More specifically, it aims to give extensive background on econometric techniques, their implementation and usage in a high level statistical package (R-studio). Each student is required to prepare a project to show their skills developed in this course.
Learning Outcomes	The students who succeeded in this course; Will be able to collect data related to social and economic topics. Will be able to get raw data ready for statistical and econometric analysis. Will be able to build econometric models that describe the data generating process behind data. Will be able to interpret the results that are obtained through econometric analysis. Will be able to conduct an independent empirical research project from start to finish. Will be able to use an econometrics software (Eviews) to make statistical and econometric analysis.
Course Description	Econometrics can be defined as the “application of statistics to the analysis of economic phenomena”. The knowledge of econometrics is essential to test economic theories and to understand empirical work being done in Economics. The course will teach how to do empirical work by using examples drawn from various fields in economics. It will also focus on various types of economic data, how one can obtain them, and how they may be used. Topics include regression analysis, ordinary least squares, hypothesis testing, choosing independent variables and functional form, multicollinearity, serial correlation and heteroskedasticity. To aid in empirical work Eviews will be used.
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses	X
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Mathematical and Statistical Foundations	HGL, Ch 1 & Appendix B, C
2	Introduction to Eviews 1	GHL, Ch 1
3	Introduction to Eviews 2	GHL, Ch 1
4	Simple Regression Model	HGL, Ch 2 & GHL Ch 2
5	Interval Estimation and Hypothesis Testing	HGL, Ch 3 & GHL Ch 3
6	Estimation and Goodness-of-Fit	HGL, Ch 4 & GHL Ch 4
7	Multivariate Regression Model 1	HGL, Ch 5 & GHL Ch 5
8	Midterm Exam
9	Multivariate Regression Model 2	HGL, Ch 6 & GHL Ch 6
10	Dummy Variable Model	HGL, Ch 7 & GHL Ch 7
11	Heteroskedasticity	HGL, Ch 8 & GHL Ch 8
12	Multicollinearity	Stock and Watson, Ch 6-7
13	Time Series: Stationarity	HGL, Ch 9 & GHL Ch 9
14	Autocorrelation	HGL, Ch 9 & GHL Ch 9
15	Qualitative Variables	HGL, Ch 16 & GHL Ch 16
16	Additional Topic(s) (Optional and Time Permitting)

Course Notes/Textbooks	C.Hill, W. Griffith and G. Lim: (HGL). (2018). Principles of Econometrics. 5th Edition. Wiley. W.Griffith, C.Hill and G.Lim: (GHL). (2018). Using EViews For Principles of Econometrics. 5th Edition. Wiley Stock, J. H., & Watson, M. W. (2020). Introduction to Econometrics. 4th Edition, Global Edition: Pearson.
Suggested Readings/Materials	Peter E. Kennedy, A Guide to Econometrics (5th Edition)

Semester Activities	Number	Weigthing
Participation	16	10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	18	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	16	3	48
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	1	24	24
Seminar / Workshop			0
Oral Exam			0
Midterms	1	30	30
Final Exam	1	30	30
		Total	180

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.	-	-	-	-	-
2	To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.	-	-	-	-	-
3	To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.	-	-	-	-	-
4	To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.	-	-	-	-	X
5	To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.	-	-	-	-	X
6	To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.	-	-	-	X	-
7	To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.	-	-	-	-	-
8	To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.	-	-	-	-	-
9	To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.	-	-	X	-	-
10	To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.	-	-	-	-	-
11	To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.	-	-	-	-	-
12	To be able to speak a second foreign language at a medium level of fluency efficiently.	-	-	-	-	-
13	To be able to relate the knowledge accumulated throughout the human history to their field of expertise.	-	-	-	-	-