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This course provides tools for empirical work with time series data and an introduction into the theoretical foundation of time series models. Much of statistical methodology is concerned with models in which the observations are assumed to be independent. End of this course, students will be able to analyze univariate and multivariate time series data using software as well as pursue research in this area.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: Middle East Technical University
|1. INTRODUCTION + STATIONARITY||00:40:00|
|2. AUTOCORRELATION, PARTIAL AUTOCORRELATION, RANDOM SHOCK FORM, INVERTED FORM||00:35:00|
|3. STATIONARY TIME SERIES PROCESSES (ARMA PROCESSES)||00:55:00|
|4. MODEL INDETIFICATION AND NON-STATIONARY TIME SERIES MODELS||01:15:00|
|5. UNIT ROOT TESTS||00:55:00|
|6. SEASONAL TIME SERIES MODELS||01:00:00|
|7. FORECASTING ARIMA FORECASTING + EXPONENTIAL SMOOTHING||01:00:00|
|9. DIAGNOSTIC CHECKING||00:35:00|
|10. MODELING VOLATILITY BY ARCH-GARCH MODELS||00:50:00|
|11. VAR MODELS AND GRANGER CAUSALITY||00:56:00|
|Submit Your Assignment||00:00:00|
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