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