4.8.1.2.5. statsmodels.tsa.api.DynamicVAR¶
-
class
statsmodels.tsa.api.
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
4.8.1.2.5.1. 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 () |
4.8.1.2.5.2. Attributes¶
nobs |
|
result_index |