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| EKO3011 | Time Series-I | 3+0+0 | ECTS:6 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of ECONOMETRICS | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Arş. Gör. Serkan SAMUT | | Co-Lecturer | Prof. Dr. Rahmi YAMAK | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course is about forecasting and some of the statistical techniques that can be used to produce forecasts. Main purpose of this course is to present forecasting principles and applications in a comprehensive way. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | understand mathematical and statistical techniques that are used in time series analysis. | 1,3 | 1, | | LO - 2 : | recognize when and how to use these techniques. | 1,3 | 1, | | LO - 3 : | produce forecasts by using time series. | 1,3 | 1, | | LO - 4 : | evaluate various forecasts and determine the best. | 1,3 | 1, | | LO - 5 : | produce micro or macro policies based on series investigated and forecast chosen. | 1,3 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| EViews Basics: Introduction to EViews, workfile basics, sample, using expressions, working with series, scalars and graphs, data objects, importing data, exporting data. Time series and cross sectional data, graphical summaries, time plots and time series patterns: trend, cyclical, seasonal and irregular pattern. Numerical summaries: univariate statistics, bivariate statistics. Measuring forecast accuracy: mean error, mean absolute error, mean squared error, root mean squared error, mean percentage error, mean absolute percentage error and Theil's U statistic. Transformations adjustments: mathematical (square root, cube root, negative reciprocal and logarithmic) transformations adjustments. Moving averages: simple, centered, double and weighted moving averages. Time series decomposition: classical additive decomposition, classical multiplicative decomposition, finding and interpreting seasonal index, deseasonalizing the data, forecasting with decomposition methods. Exponential smoothing methods: simple exponential smoothing, Holt's exponential smoothing, Winters' exponential smoothing. Introduction to forecasting with regression methods: simple regression, the least squares estimation, the correlation coefficient, simple regression and the correlation coefficient, forecasting using the simple regression model. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | EViews Basics: Introduction to EViews, workfile basics, sample, working with series, scalars and graphs, importing data, exporting data. | | | Week 2 | Time series and cross sectional data, graphical summaries, time plots and time series patterns: trend, cyclical, seasonal and irregular pattern. | | | Week 3 | Numerical summaries: univariate statistics, bivariate statistics. | | | Week 4 | Measuring forecast accuracy: mean error, mean absolute error, mean squared error, root mean squared error, mean percentage error, mean absolute percentage error and Theil's U statistic. | | | Week 5 | Transformations and calendar adjustments: mathematical (square root, cube root, negative reciprocal and logarithmic) transformations and calendar (month length and trading day) adjustments | | | Week 6 | Moving averages: simple, centered, double and weighted moving averages. | | | Week 7 | Time series decomposition: classical additive decomposition. | | | Week 8 | Time series decomposition: classical multiplicative decomposition. | | | Week 9 | Mid-term exam | | | Week 10 | finding and interpreting seasonal index, deseasonalizing the data. | | | Week 11 | forecasting with decomposition methods. | | | Week 12 | Exponential smoothing methods: simple exponential smoothing, adaptive-response rate simple exponential smoothing. | | | Week 13 | Holt's exponential smoothing. | | | Week 14 | Winters' exponential smoothing. | | | Week 15 | Introduction to forecasting with regression methods: simple regression, the least squares estimation. the correlation coefficient, simple regression and the correlation coefficient, forecasting Tsing the simple regression model. | | | Week 16 | End-of-term exam | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 12.11.2023 | 1 | 50 | | End-of-term exam | 16 | 25.01.2024 | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 6 | 14 | 84 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 11 | 2 | 22 | | Arasınav | 1 | 1 | 1 | | Uygulama | 0 | 0 | 0 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 10 | 3 | 30 | | Dönem sonu sınavı | 1 | 1 | 1 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 180 |
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