class statsmodels.tsa.ar_model.AR(endog, dates=None, freq=None, missing='none')[source]

Autoregressive AR(p) model

  • endog (array-like) – 1-d endogenous response variable. The independent variable.
  • dates (array-like of datetime, optional) – An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex.
  • freq (str, optional) – The frequency of the time-series. A Pandas offset or ‘B’, ‘D’, ‘W’, ‘M’, ‘A’, or ‘Q’. This is optional if dates are given.
  • missing (str) – Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’


fit([maxlag, method, ic, trend, …]) Fit the unconditional maximum likelihood of an AR(p) process.
from_formula(formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe.
hessian(params) Returns numerical hessian for now.
information(params) Not Implemented Yet
initialize() Initialize (possibly re-initialize) a Model instance.
loglike(params) The loglikelihood of an AR(p) process
predict(params[, start, end, dynamic]) Returns in-sample and out-of-sample prediction.
score(params) Return the gradient of the loglikelihood at params.
select_order(maxlag, ic[, trend, method]) Select the lag order according to the information criterion.


endog_names Names of endogenous variables