Estimates time-varying vector autoregression (VAR(p)) using equation-by-equation least squares
Parameters: | data : pandas.DataFrame lag_order : int, default 1 window : int window_type : {‘expanding’, ‘rolling’} min_periods : int or None
trend : {‘c’, ‘nc’, ‘ct’, ‘ctt’}
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Returns: | **Attributes:** : coefs : Panel
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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() |
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
nobs | |
result_index |