UsefulLinks
Statistics
Time Series Analysis
1. Introduction to Time Series
2. Foundational Concepts and Data Preparation
3. Descriptive Analysis and Decomposition
4. Stationarity and Unit Root Analysis
5. Autocorrelation and Dependence Structure
6. Classical Forecasting Methods
7. ARIMA Modeling
8. Seasonal ARIMA Models
9. Advanced Univariate Models
10. Volatility Modeling
11. Multivariate Time Series
12. State Space Models and Kalman Filtering
13. Machine Learning for Time Series
14. Forecasting Evaluation and Model Selection
15. Practical Forecasting Considerations
16. Software and Implementation
11.
Multivariate Time Series
11.1.
Vector Autoregression
11.1.1.
VAR Model Structure
11.1.2.
Lag Order Selection
11.1.3.
Parameter Estimation
11.1.4.
Impulse Response Functions
11.1.5.
Forecast Error Variance Decomposition
11.2.
Granger Causality
11.2.1.
Definition and Testing
11.2.2.
Interpretation
11.2.3.
Limitations
11.3.
Cointegration
11.3.1.
Definition and Concepts
11.3.2.
Engle-Granger Method
11.3.3.
Johansen Method
11.3.4.
Error Correction Models
11.4.
Vector Error Correction Models
11.4.1.
VECM Structure
11.4.2.
Estimation Methods
11.4.3.
Forecasting with VECM
11.5.
Structural VAR Models
11.5.1.
Identification Restrictions
11.5.2.
Recursive Models
11.5.3.
Long-Run Restrictions
11.6.
Factor Models
11.6.1.
Dynamic Factor Models
11.6.2.
Principal Component Analysis
11.6.3.
Factor-Augmented VAR
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12. State Space Models and Kalman Filtering