A 1-d endogenous response variable. The dependent variable.

The number of lags to include in the model if an integer or the list of lag indices to include. For example, [1, 4] will only include lags 1 and 4 while lags=4 will include lags 1, 2, 3, and 4. None excludes all AR lags, and behave identically to 0.

The trend to include in the model:

‘n’ - No trend.

‘c’ - Constant only.

‘t’ - Time trend only.

‘ct’ - Constant and time trend.

Flag indicating whether to include seasonal dummies in the model. If seasonal is True and trend includes ‘c’, then the first period is excluded from the seasonal terms.

Exogenous variables to include in the model. Must have the same number of observations as endog and should be aligned so that endog[i] is regressed on exog[i].

Initial observations to exclude from the estimation sample. If None, then hold_back is equal to the maximum lag in the model. Set to a non-zero value to produce comparable models with different lag length. For example, to compare the fit of a model with lags=3 and lags=1, set hold_back=3 which ensures that both models are estimated using observations 3,…,nobs. hold_back must be >= the maximum lag in the model.

The period of the data. Only used if seasonal is True. This parameter can be omitted if using a pandas object for endog that contains a recognized frequency.

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’.

A deterministic process. If provided, trend and seasonal are ignored. A warning is raised if trend is not “n” and seasonal is not False.

Flag indicating whether to use the v0.11 names or the v0.12+ names.

Deprecated since version 0.13: old_names is deprecated and will be removed after 0.14 is released. You must update any code reliant on the old variable names to use the new names.