ECONOMICS
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
ECO4442	Time Series Econometrics	Fall	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Hybrid
Course Coordinator :	Dr. Öğr. Üyesi EMİNE ZEREN TAŞPINAR
Course Objectives:	After reviewing the basic principles of econometrics and Ordinary Least Square (OLS) methods, time series models will be introduced. The students will learn autocorrelation analysis, stationarity, difference equations, lag operator, making a non-stationary series stationary by differencing methods, statistical models for autoregressive (AR) process, statistical models for moving average (MA) processes, statistical models for autoregressive moving average (ARMA) processes, non-stationarity and integration processes, statistical models for Autoregressive Integrated Moving Average (ARIMA) processes, Seasonal Box-Jenkins ARIMA models, Unit roots, Dickey-Fuller and Phillips-Peron unit root tests, cointegration and tests for cointegration, seasonality and trends, removing seasonality and trends, seasonal integration and cointegration tests, Autoregressive Conditional Heteroscedasticity (ARCH and GARCH) models during the semester. At the end of the semester, the students will learn how to make forecasting based on past and current data. The students will learn to apply their theoretical knowledge by using related econometric packages. The students will be given a project in which they investigate the long-term relationship between the variables by using their both theoretical and practical knowledge.

Learning Outcomes

The students who have succeeded in this course;
1. How to examine economic questions on dynamic causal relationship between economic variables and forecasting future values of economic variables through time-series econometrics.
2. Basic concepts and terminology of time-series econometrics.
3. Basics about the stationary time series models.
4. Basics about non-stationary time series models.
5. Estimation of time-series models’ parameters.
6. Time series components and seasonal adjustments.
7. Forecasting.
8. Running times series regressions, doing seasonal adjustment and forecasting in R.

Course Content

After reviewing the basic principles of econometrics and Ordinary Least Square (OLS) methods, time series models will be introduced. The students will learn autocorrelation analysis, stationarity, difference equations, lag operator, making a non-stationary series stationary by differencing methods, statistical models for autoregressive (AR) process, statistical models for moving average (MA) processes, statistical models for autoregressive moving average (ARMA) processes, non-stationarity and integration processes, statistical models for Autoregressive Integrated Moving Average (ARIMA) processes, Seasonal Box-Jenkins ARIMA models, Unit roots, Dickey-Fuller and Phillips-Peron unit root tests, cointegration and tests for cointegration, seasonality and trends, removing seasonality and trends, seasonal integration and cointegration tests, Autoregressive Conditional Heteroscedasticity (ARCH and GARCH) models during the semester. At the end of the semester, the students will learn how to make forecasting based on past and current data. The students will learn to apply their theoretical knowledge by using related econometric packages. The students will be given a project in which they investigate the long-term relationship between the variables by using their both theoretical and practical knowledge.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction, Basic Components, Ordinary Least Square (OLS) Methods	Chapter 1 Brockwell
2)	Difference Equations and Solutions, the Use of Difference Equations in Time Series Analysis	Chapter 1 Enders
3)	Stationarity and Unit Root Tests of Stationarity (Dickey-Fuller; Augmented Dickey Fuller; Phillips-Peron)	Chapter 2 Brockwell
4)	Autoregressive Processes (AR), Moving Average Processes (MA), Autoregressive Moving Average Processes (ARMA)	Chapter 2 Enders, Chapter 3 Brockwell
5)	Autoregressive Integrated Moving Average (ARIMA)Processes	Chapter 2 Enders
6)	Seasonality, Removing Seasonality	Chapter 2 Enders
7)	Seasonal Box-Jenkins ARIMA Models	Chapter 2 Enders
8)	Introduction to Volatility Models in Time Series	Chapter 3 Enders
9)	Autoregressive Conditional Heteroscedasticity (ARCH and GARCH) Models	Chapter 3 Enders
10)	Trend and Structural Break Analysis	Chapter 4 Enders
11)	Cointegration and Error Correction Models	Chapter 6 Enders
12)	Dynamic Models in Time Series	Chapter 5 Enders
13)	Forecasting	Chapter 10 Brockwell
14)	General review, evaluation and term-project presentations	Lecture Notes

Sources

Course Notes / Textbooks:	Walter Enders, Applied Econometric Time Series, 4th Edition, Wiley, 2014. Peter J. Brockwell ve Richard A. Davis, Introduction to Time Series and Forecasting, Switzerland: Springer, 2016.
References:	John E. Hanke ve Dean W. Wichern, Business Forecasting, New Jersey: Pearson, 2009.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 40
Midterms	1	% 30
Final	1	% 30
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 30
PERCENTAGE OF FINAL WORK		% 70
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	8	112
Presentations / Seminar	1	1	1
Midterms	1	2	2
Final	1	2	2
Total Workload			159

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	As a world citizen, she is aware of global economic, political, social and ecological developments and trends.	2
2)	He/she is equipped to closely follow the technological progress required by global and local dynamics and to continue learning.	3
3)	Absorbs basic economic principles and analysis methods and uses them to evaluate daily events.	4
4)	Uses quantitative and statistical tools to identify economic problems, analyze them, and share their findings with relevant stakeholders.	5
5)	Understands the decision-making stages of economic units under existing constraints and incentives, examines the interactions and possible future effects of these decisions.	5
6)	Comprehends new ways of doing business using digital technologies. and new market structures.	1
7)	Takes critical approach to economic and social problems and develops analytical solutions.	2
8)	Has the necessary mathematical equipment to produce analytical solutions and use quantitative research methods.	5
9)	In the works he/she contributes, observes individual and social welfare together and with an ethical perspective.	1
10)	Deals with economic problems with an interdisciplinary approach and seeks solutions by making use of different disciplines.	2
11)	Generates original and innovative ideas in the works she/he contributes as part of a team.	1