| 1 |
Introduction to Time Series: Concepts, Data Types, Stationarity, and the Wold Decomposition |
| 2 |
Autocovariance, ACF, PACF, and White Noise Processes |
| 3 |
AR(p) Models: Properties, Estimation, and Stability Conditions |
| 4 |
MA(q) and ARMA(p,q) Models: Invertibility, Duality, and Yule-Walker Equations |
| 5 |
Box-Jenkins Methodology: Identification, Estimation, Diagnostic Checking, and Forecasting |
| 6 |
Non-Stationarity and Unit Roots: ADF, Phillips-Perron, and KPSS Tests |
| 7 |
Midterm Examination and Review |
| 8 |
Structural Breaks: Chow Test, Zivot-Andrews, and Bai-Perron Tests |
| 9 |
Cointegration Theory: Engle-Granger Approach and Error Correction Models |
| 10 |
Johansen Procedure: Trace and Maximum Eigenvalue Tests, VECM Estimation |
| 11 |
Vector Autoregression (VAR): Specification, Estimation, and Granger Causality |
| 12 |
Impulse Response Functions and Forecast Error Variance Decomposition |
| 13 |
Volatility Modelling: ARCH, GARCH, EGARCH, and GJR-GARCH Models |
| 14 |
Forecast Evaluation: RMSE, MAE, Diebold-Mariano Test, and Applied Research Workshop |
