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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.

Assessment

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

Course Curriculum

Module: 01
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
Module: 02
5. UNIT ROOT TESTS 00:55:00
6. SEASONAL TIME SERIES MODELS 01:00:00
7. FORECASTING ARIMA FORECASTING + EXPONENTIAL SMOOTHING 01:00:00
8. ESTIMATION 00:35:00
Module: 03
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
12. COINTEGRATION 00:35:00
Assessment
Submit Your Assignment 00:00:00

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