statsmodels.tsa.vector_ar.dynamic.DynamicVAR
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class statsmodels.tsa.vector_ar.dynamic.DynamicVAR(data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None) [source]
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Estimates time-varying vector autoregression (VAR(p)) using equation-by-equation least squares
| Parameters: |
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data (pandas.DataFrame) –
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lag_order (int, default 1) –
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window (int) –
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window_type ({'expanding', 'rolling'}) –
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min_periods (int or None) – Minimum number of observations to require in window, defaults to window size if None specified
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trend ({'c', 'nc', 'ct', 'ctt'}) – TODO
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| Returns: |
- **Attributes**
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coefs (Panel) – items : coefficient names major_axis : dates minor_axis : VAR equation names
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Methods
T() | Number of time periods in results |
coefs() | Return dynamic regression coefficients as Panel |
equations() | |
forecast([steps]) | Produce dynamic forecast |
plot_forecast([steps, figsize]) | Plot h-step ahead forecasts against actual realizations of time series. |
r2() | Returns the r-squared values. |
resid() | |
Attributes