Learning Vector Autoregression in 6 weeks
Posted by: admin 5 months, 3 weeks ago
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Here is the program
Week 1: Introduction to Time Series Analysis
Day 1-2:
- Overview of Time Series Data:
- Definition of time series data
- Characteristics and patterns in time series data
Day 3-4:
- Time Series Components:
- Trend, seasonality, and randomness in time series data
- Decomposition of time series data
Day 5-7:
- Statistical Properties of Time Series:
- Stationarity, autocorrelation, and white noise
- Augmented Dickey-Fuller (ADF) test for stationarity in Python
Week 2: Fundamentals of Vector Autoregression (VAR)
Day 1-3:
- Introduction to VAR Model:
- What is VAR and its applications in econometrics
- Assumptions of VAR model
Day 4-5:
- Estimation and Interpretation:
- Estimating VAR models
- Interpreting VAR results
Day 6-7:
- Granger Causality and Lag Selection:
- Granger causality test
- Lag selection methods (AIC, BIC) for VAR models
Week 3: Implementing VAR in Python
Day 1-3:
- Python Libraries for Time Series Analysis:
- Introduction to pandas, NumPy, and statsmodels
- Loading and manipulating time series data in Python
Day 4-5:
- Building a VAR Model in Python:
- Implementing VAR model using statsmodels library
- Model diagnostics and interpretation
Day 6-7:
- Visualization and Forecasting with VAR:
- Plotting results and visualizing time series forecasts
- Forecasting using VAR models in Python
Week 4: Advanced VAR Modeling Techniques
Day 1-3:
- Structural VAR (SVAR):
- Understanding the concept of identifying shocks and structural modeling in VAR
- Impulse Response Analysis in SVAR
Day 4-5:
- Bayesian VAR (BVAR):
- Introduction to Bayesian approach in VAR modeling
- Implementing BVAR in Python
Day 6-7:
- VAR with Exogenous Variables:
- Extending VAR models to include exogenous variables
- Applications in finance and economics
Week 5: VAR Model Evaluation and Advanced Concepts
Day 1-3:
- Model Evaluation and Selection:
- Evaluating VAR model performance (MSE, RMSE, etc.)
- Comparison with other time series models
Day 4-5:
- Cointegration and Error Correction Models:
- Understanding cointegration and its relation to VAR
- Implementing Vector Error Correction Model (VECM) in Python
Day 6-7:
- Multivariate Time Series Analysis:
- Multivariate time series concepts beyond VAR
- Applications and case studies
Week 6: Application in Finance and Case Studies
Day 1-4:
- Financial Time Series Analysis:
- Application of VAR in finance: asset prices, macroeconomic variables, etc.
- Case studies and research papers on VAR in finance
Day 5-6:
- Student Projects and Presentations:
- Assign a project where students apply VAR to financial data
- Presentation and discussion of the findings
Day 7:
- Review and Summary:
- Recap of the entire course
- Q&A and discussion on future research directions
Throughout this program, students will be given practical exercises, assignments, and real-world datasets to work with, aligning theory with hands-on experience in Python. Additionally, office hours and support for queries will be available to aid in understanding and applying these concepts effectively.
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