tsa.vector_ar.dynamic.DynamicVAR()

statsmodels.tsa.vector_ar.dynamic.DynamicVAR

class statsmodels.tsa.vector_ar.dynamic.DynamicVAR(data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None) [source]

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 Minimum number of observations to require in window, defaults to window size if None specified trend : {‘c’, ‘nc’, ‘ct’, ‘ctt’} TODO
Returns:	Attributes: coefs : Panel items : coefficient names major_axis : dates minor_axis : VAR equation names

Attributes

nobs
result_index

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