statsmodels.tsa.stattools.pacf¶

statsmodels.tsa.stattools.pacf(x, nlags=None, method='ywadjusted', alpha=None)[source]¶

Partial autocorrelation estimate.

Parameters:

Observations of time series for which pacf is calculated.

nlagsint, optional

Number of lags to return autocorrelation for. If not provided, uses min(10 * np.log10(nobs), nobs // 2 - 1). The returned value includes lag 0 (ie., 1) so size of the pacf vector is (nlags + 1,).

methodstr, default “ywunbiased”

Specifies which method for the calculations to use.

“yw” or “ywadjusted” : Yule-Walker with sample-size adjustment in denominator for acovf. Default.
“ywm” or “ywmle” : Yule-Walker without adjustment.
“ols” : regression of time series on lags of it and on constant.
“ols-inefficient” : regression of time series on lags using a single common sample to estimate all pacf coefficients.
“ols-adjusted” : regression of time series on lags with a bias adjustment.
“ld” or “ldadjusted” : Levinson-Durbin recursion with bias correction.
“ldb” or “ldbiased” : Levinson-Durbin recursion without bias correction.

alphafloat, optional

If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to 1/sqrt(len(x)).

Returns:

pacfndarray: The partial autocorrelations for lags 0, 1, …, nlags. Shape (nlags+1,).
confintndarray, optional: Confidence intervals for the PACF at lags 0, 1, …, nlags. Shape (nlags + 1, 2). Returned if alpha is not None.