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)
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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 : WidePanel
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 WidePanel 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
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