ECO4442 Time Series EconometricsBahçeşehir UniversityDegree Programs INTERNATIONAL FINANCEGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
INTERNATIONAL FINANCE
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
Spring
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) To correctly identify the problems and to be able to ask the correct questions 3
2) To have the ability for problem solving and to utilize analytical approach in dealing with the problems of finance 2
3) To understand and grasp the full details of theoretical arguments and counter arguments 3
4) To be fully prepared for a graduate study in finance and to have lifelong learning awareness 2
5) To be able to apply theoretical principles of finance to the realities of practical business life 1
6) To develop solutions for managerial problems by understanding the requirements of international financial markets 1
7) To think innovatively and creatively in complex situations 2
8) To be able to make decisions both locally and internationally by knowing the effects of globalization on business and social life 1
9) To have the competencies of the digital age and to use the necessary financial applications 4
10) To be able to use at least one foreign language both for communication and academic purposes 1
11) To understand the importance of business ethics and to take decisions by knowing the legal and ethical consequences of their activities in the academic world and business life 2
12) To develop an objective criticism in business and academic life and having a perspective to self-criticize 1