'Call self.model.predict with self.params as the first argument.
Parameters
exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a formula, do you want to pass
exog through the formula. Default is True. E.g., if you fit
a model y ~ log(x1) + log(x2), and transform is True, then
you can pass a data structure that contains x1 and x2 in
their original form. Otherwise, you\'d need to log the data
first.
args, kwargs :
Some models can take additional arguments or keywords, see the
predict method of the model for the details.
Returns
prediction : ndarray, pandas.Series or pandas.DataFrame
See self.model.predict'

def predict(self, exog=None, transform=True, *args, **kwargs):

    import pandas as pd
    exog_index = (exog.index if _is_using_pandas(exog, None) else None)
    if (transform and hasattr(self.model, 'formula') and (exog is not None)):
        from patsy import dmatrix
        exog = pd.DataFrame(exog)
        if (exog_index is None):
            exog_index = exog.index
        exog = dmatrix(self.model.data.design_info.builder, exog, return_type='dataframe')
        if (len(exog) < len(exog_index)):
            if (exog_index is not None):
                exog = exog.reindex(exog_index)
            else:
                import warnings
                warnings.warn('nan    rows    have    been    dropped', ValueWarning)
    if (exog is not None):
        exog = np.asarray(exog)
        if ((exog.ndim == 1) and ((self.model.exog.ndim == 1) or (self.model.exog.shape[1] == 1))):
            exog = exog[:, None]
        exog = np.atleast_2d(exog)
    predict_results = self.model.predict(self.params, exog, *args, **kwargs)
    if ((exog_index is not None) and (not hasattr(predict_results, 'predicted_values'))):
        if (predict_results.ndim == 1):
            return pd.Series(predict_results, index=exog_index)
        else:
            return pd.DataFrame(predict_results, index=exog_index)
    else:
        return predict_results
