INTERNATIONAL FINANCE | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
ECO4442 | Time Series Econometrics | Spring | 3 | 0 | 3 | 4 |
The course opens with the approval of the Department at the beginning of each semester |
Language of instruction: | En |
Type of course: | Departmental Elective |
Course Level: | Bachelor |
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. |
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. |
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. |
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 |
Course Notes: | 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. |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | % 0 | |
Laboratory | % 0 | |
Application | % 0 | |
Field Work | % 0 | |
Special Course Internship (Work Placement) | % 0 | |
Quizzes | % 0 | |
Homework Assignments | % 0 | |
Presentation | % 0 | |
Project | 1 | % 40 |
Seminar | % 0 | |
Midterms | 1 | % 30 |
Preliminary Jury | % 0 | |
Final | 1 | % 30 |
Paper Submission | % 0 | |
Jury | % 0 | |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 30 | |
PERCENTAGE OF FINAL WORK | % 70 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 14 | 8 | 112 |
Presentations / Seminar | 1 | 1 | 1 |
Project | 0 | 0 | 0 |
Homework Assignments | 0 | 0 | 0 |
Quizzes | 0 | 0 | 0 |
Preliminary Jury | 0 | 0 | 0 |
Midterms | 1 | 2 | 2 |
Paper Submission | 0 | 0 | 0 |
Jury | 0 | 0 | 0 |
Final | 1 | 2 | 2 |
Total Workload | 159 |
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 | |
2) | To have the ability for problem solving and to utilize analytical approach in dealing with the problems of finance | |
3) | To understand and grasp the full details of theoretical arguments and counter arguments | |
4) | To be fully prepared for a graduate study in finance and to have lifelong learning awareness | |
5) | To be able to apply theoretical principles of finance to the realities of practical business life | |
6) | To develop solutions for managerial problems by understanding the requirements of international financial markets | |
7) | To think innovatively and creatively in complex situations | |
8) | To be able to make decisions both locally and internationally by knowing the effects of globalization on business and social life | |
9) | To have the competencies of the digital age and to use the necessary financial applications | |
10) | To be able to use at least one foreign language both for communication and academic purposes | |
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 | |
12) | To develop an objective criticism in business and academic life and having a perspective to self-criticize |