statsmodels.tsa.stattools.pacf¶
-
statsmodels.tsa.stattools.
pacf
(x, nlags=40, method='ywunbiased', alpha=None)[source]¶ Partial autocorrelation estimate.
- Parameters
- xarray_like
Observations of time series for which pacf is calculated.
- nlags
int
,optional
The largest lag for which the pacf is returned.
- method
str
,optional
Specifies which method for the calculations to use.
‘yw’ or ‘ywunbiased’ : Yule-Walker with bias correction in denominator for acovf. Default.
‘ywm’ or ‘ywmle’ : Yule-Walker without bias correction.
‘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-unbiased’ : regression of time series on lags with a bias adjustment.
‘ld’ or ‘ldunbiased’ : Levinson-Durbin recursion with bias correction.
‘ldb’ or ‘ldbiased’ : Levinson-Durbin recursion without bias correction.
- alpha
float
,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
See also
statsmodels.tsa.stattools.acf
Estimate the autocorrelation function.
statsmodels.tsa.stattools.pacf
Partial autocorrelation estimation.
statsmodels.tsa.stattools.pacf_yw
Partial autocorrelation estimation using Yule-Walker.
statsmodels.tsa.stattools.pacf_ols
Partial autocorrelation estimation using OLS.
statsmodels.tsa.stattools.pacf_burg
Partial autocorrelation estimation using Burg’s method.
Notes
Based on simulation evidence across a range of low-order ARMA models, the best methods based on root MSE are Yule-Walker (MLW), Levinson-Durbin (MLE) and Burg, respectively. The estimators with the lowest bias included included these three in addition to OLS and OLS-unbiased.
Yule-Walker (unbiased) and Levinson-Durbin (unbiased) performed consistently worse than the other options.