statsmodels.regression.linear_model.burg¶
-
statsmodels.regression.linear_model.
burg
(endog, order=1, demean=True)[source]¶ Compute Burg’s AP(p) parameter estimator.
Parameters: endog : array_like
The endogenous variable.
order : int, optional
Order of the AR. Default is 1.
demean : bool, optional
Flag indicating to subtract the mean from endog before estimation.
Returns: rho : ndarray
The AR(p) coefficients computed using Burg’s algorithm.
sigma2 : float
The estimate of the residual variance.
See also
yule_walker
- Estimate AR parameters using the Yule-Walker method.
Notes
AR model estimated includes a constant that is estimated using the sample mean (see [R68]). This value is not reported.
References
[R68] (1, 2) Brockwell, P.J. and Davis, R.A., 2016. Introduction to time series and forecasting. Springer. Examples
>>> import statsmodels.api as sm >>> from statsmodels.datasets.sunspots import load >>> data = load(as_pandas=True) >>> rho, sigma2 = sm.regression.linear_model.burg(data.endog, order=4)
>>> rho array([ 1.30934186, -0.48086633, -0.20185982, 0.05501941]) >>> sigma2 271.2467306963966