Week 1 to week 6 in learning VAR
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Week 1: Introduction to Time Series Analysis
Overview of Time Series Data:
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# Load necessary libraries import pandas as pd import matplotlib.pyplot as plt # Load and visualize time series data data = pd.read_csv('your_time_series_data.csv') plt.figure(figsize=(10, 6)) plt.plot(data['Date'], data['Value']) plt.title('Time Series Data') plt.xlabel('Date') plt.ylabel('Value') plt.show() Time Series Components:
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# Decomposition of time series data from statsmodels.tsa.seasonal import seasonal_decompose result = seasonal_decompose(data['Value'], model='additive', period=12) result.plot() plt.show() Statistical Properties of Time Series:
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# Stationarity check using Augmented Dickey-Fuller test from statsmodels.tsa.stattools import adfuller result = adfuller(data['Value']) print('ADF Statistic:', result[0]) print('p-value:', result[1]) print('Critical Values:', result[4]) Week 2: Fundamentals of Vector Autoregression (VAR)
Introduction to VAR Model:
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# Import VAR model from statsmodels from statsmodels.tsa.vector_ar.var_model import VAR # Create VAR model model = VAR(data) Estimation and Interpretation:
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# Fit the VAR model results = model.fit() # Summary of the VAR model print(results.summary()) Granger Causality and Lag Selection:
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# Granger causality test from statsmodels.tsa.stattools import grangercausalitytests max_lag = 4 # maximum lag to test causality granger_test_result = grangercausalitytests(data, max_lag) Week 3: Implementing VAR in Python
Building a VAR Model in Python:
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# Implementing VAR model using statsmodels library model = VAR(data) results = model.fit(maxlags=4) # fitting the model with selected maximum lag Visualization and Forecasting with VAR:
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# Plotting results and visualizing time series forecasts results.plot_forecast(10) Implementing Impulse Response Analysis:
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# Impulse Response Analysis irf = results.irf(10) irf.plot(orth=False) This breakdown provides code snippets for key concepts covered in the weekly plan. For the complete course, you would expand upon these snippets, incorporate explanations, provide datasets, and encourage students to apply these techniques to various time series datasets and financial data, ensuring they understand the theory and practical implementation of VAR models in Python.
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