Learning Vector Autoregression in 6 weeks

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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.

• ← week 6 Finance with Python
• Week 1 to week 6 in learning VAR →