SUR.predict(params: ndarray | DataArray | DataFrame | Series, *, equations: Mapping[str, Mapping[str, ndarray | DataArray | DataFrame | Series]] | None =
None, data: DataFrame | None =
None, eval_env: int =
1) DataFrame ¶
Predict values for additional data
- params: ndarray | DataArray | DataFrame | Series¶
Model parameters (nvar by 1)
- equations: Mapping[str, Mapping[str, ndarray | DataArray | DataFrame | Series]] | None =
Dictionary-like structure containing exogenous and endogenous variables. Each key is an equations label and must match the labels used to fir the model. Each value must be a dictionary with keys “exog” and “endog”. If predictions are not required for one of more of the model equations, these keys can be omitted.
- data: DataFrame | None =
Values to use when making predictions from a model constructed from a formula
- eval_env: int =
Depth to use when evaluating formulas.
predictions – Fitted values from supplied data and parameters
- Return type:¶
If data is not none, then equations must be none. Predictions from models constructed using formulas can be computed using either equations, which will treat these are arrays of values corresponding to the formula-process data, or using data which will be processed using the formula used to construct the values corresponding to the original model specification.
When using exog and endog, the regressor array for a particular equation is assembled as [equations[eqn][“exog”], equations[eqn][“endog”]] where eqn is an equation label. These must correspond to the columns in the estimated model.