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

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