statsmodels.regression.linear_model.yule_walker¶
-
statsmodels.regression.linear_model.
yule_walker
(x, order=1, method='unbiased', df=None, inv=False, demean=True)[source]¶ Estimate AR(p) parameters from a sequence using the Yule-Walker equations.
Unbiased or maximum-likelihood estimator (mle)
- Parameters
- xarray_like
A 1d array.
- order
int
,optional
The order of the autoregressive process. Default is 1.
- method
str
,optional
Method can be ‘unbiased’ or ‘mle’ and this determines denominator in estimate of autocorrelation function (ACF) at lag k. If ‘mle’, the denominator is n=X.shape[0], if ‘unbiased’ the denominator is n-k. The default is unbiased.
- df
int
,optional
Specifies the degrees of freedom. If df is supplied, then it is assumed the X has df degrees of freedom rather than n. Default is None.
- invbool
If inv is True the inverse of R is also returned. Default is False.
- demeanbool
True, the mean is subtracted from X before estimation.
- Returns
See also
burg
Burg’s AR estimator.
Notes
See https://en.wikipedia.org/wiki/Autoregressive_moving_average_model for further details.
Examples
>>> import statsmodels.api as sm >>> from statsmodels.datasets.sunspots import load >>> data = load(as_pandas=False) >>> rho, sigma = sm.regression.yule_walker(data.endog, order=4, ... method="mle")
>>> rho array([ 1.28310031, -0.45240924, -0.20770299, 0.04794365]) >>> sigma 16.808022730464351