Learning Vector Autoregression in 6 weeks


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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