7.8.2.1.1. statsmodels.tsa.ar_model.AR¶

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

Autoregressive AR(p) model

Parameters:

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.’

__init__(endog, dates=None, freq=None, missing='none')[source]¶

Methods

__init__(endog[, dates, freq, missing])
fit([maxlag, method, ic, trend, ...])	Fit the unconditional maximum likelihood of an AR(p) process.
from_formula(formula, data[, subset])	Create a Model from a formula and dataframe.
hessian(params)	Returns numerical hessian for now.
information(params)	Not Implemented Yet
initialize()
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.

Attributes

endog_names
exog_names