statsmodels.tsa.stattools.levinson_durbin(s, nlags=10, isacov=False)[source]

Levinson-Durbin recursion for autoregressive processes


s : array_like

If isacov is False, then this is the time series. If iasacov is true then this is interpreted as autocovariance starting with lag 0

nlags : integer

largest lag to include in recursion or order of the autoregressive process

isacov : boolean

flag to indicate whether the first argument, s, contains the autocovariances or the data series.


sigma_v : float

estimate of the error variance ?

arcoefs : ndarray

estimate of the autoregressive coefficients

pacf : ndarray

partial autocorrelation function

sigma : ndarray

entire sigma array from intermediate result, last value is sigma_v

phi : ndarray

entire phi array from intermediate result, last column contains autoregressive coefficients for AR(nlags) with a leading 1


This function returns currently all results, but maybe we drop sigma and phi from the returns.

If this function is called with the time series (isacov=False), then the sample autocovariance function is calculated with the default options (biased, no fft).