regression.linear_model.yule_walker()

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 X using Yule-Walker equation.

Unbiased or maximum-likelihood estimator (mle)

See, for example:

http://en.wikipedia.org/wiki/Autoregressive_moving_average_model

Parameters:

X : array-like 1d array order : integer, optional The order of the autoregressive process. Default is 1. method : string, 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 : integer, 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. inv : bool If inv is True the inverse of R is also returned. Default is False. demean : bool True, the mean is subtracted from X before estimation.

Returns:

rho : The autoregressive coefficients sigma : TODO

Examples

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>>> import statsmodels.api as sm >>> from statsmodels.datasets.sunspots import load >>> data = load() >>> rho, sigma = sm.regression.yule_walker(data.endog,                                    order=4, method="mle")

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>>> rho array([ 1.28310031, -0.45240924, -0.20770299,  0.04794365]) >>> sigma 16.808022730464351