7.8.2.3.3. 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)[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 : WidePanel
items : coefficient names major_axis : dates minor_axis : VAR equation names
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__init__(data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None)[source]¶
Methods
T()Number of time periods in results __init__(data[, lag_order, window, ...])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
nobsresult_index-