ECON 416 | Course Introduction and Application Information - Department of Mathematics

Course Name

Time Series Analysis

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
ECON 416	Fall/Spring	3	0	3	6

Prerequisites

ECON 301 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)

Course Objectives

This course is an introduction the econometric analysis of time series. Previous exposition to an introductory econometrics course is assumed, but the material is designed to be as selfcontained as possible. The focus of the class is mostly empirical. The student will be asked to apply his/her knowledge of the topics to real data on a weekly basis, and carry out a research project on a topic of his/her choice.

Learning Outcomes

The students who succeeded in this course;

Will be able to define the specific properties of time series data.
Will be able to use econometric tools that are specifically designed to analyze time series data.
Will be able to build econometric models that describe the behavior of time series.
Will be able to identify problems with existing econometric models.
Will be able to employ the econometric tools necessary to solve problems with existing econometric models.
Will be able to interpret the results that are obtained through econometric analysis.
Will be able to pursue an independent empirical research project from start to finish.

Course Description

The class covers the theory behind a variety of topics in time series econometrics, and introduces the student to a large number of time series applications. Class exposition is evenly divided between theory and applications, but applications are given priority in assignements and exams. After a brief review of statistical and econometric basics, we discuss the use of difference equations and lag operators. Stationary ARMA models are covered in great detail, and so are ARCH, GARCH, and VAR techniques. The student is also exposed to nonstationary time series, unit roots, and ARIMA models. The class ends with discussions on cointegration and forecasting.

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

Week	Subjects	Related Preparation	Learning Outcome
1	Review of statistical concepts and regression basics
2	Difference equations, lag operators
3	Foundations of time series econometrics
4	Topics in Linear Regression
5	ARMA Modeling 1
6	ARMA Modeling 2
7	ARMA Modeling 3
8	Midterm
9	Nonstationarity, unit roots, and ARIMA Models
10	Autoregressive conditional heteroscedasticity: ARCH and GARCH models
11	Stationary vector models: VAR pt. 1
12	Stationary vector models: VAR pt. 2
13	Cointegration and common trends
14	Forecasting with time series
15	Additional topic (optional and time permitting)
16	Presentations

Course Notes/Textbooks	Walter Enders, Applied Econometric Time Series (Second Edition), Wiley.
Suggested Readings/Materials	Two additional sources are recommended, and may provide additional class material: \n• Paul S. P. Cowpertwait and Andrew V. Metcalfe, Introductory Time Series with R.\n• Brockwell and Davis, Introduction to Time Series and Forecasting (Second Edition).

Semester Activities	Number	Weigthing
Participation	16	10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	2	40
Seminar / Workshop
Oral Exams
Midterm	1	20
Final Exam	1	30
Total

Weighting of Semester Activities on the Final Grade		70
Weighting of End-of-Semester Activities on the Final Grade		30
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			0
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	2	35	70
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	27	27
		Total	165

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	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.		-	-	-	-	-
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.		-	-	-	-	-
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.		-	-	-	X	-
13	To be able to relate the knowledge accumulated throughout the human history to their field of expertise.		-	-	-	-	-