linearmodels.panel.model.PooledOLS.predict¶
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PooledOLS.predict(params: ndarray | DataArray | DataFrame | Series, *, exog: PanelData | ndarray | DataArray | DataFrame | Series | None =
None, data: PanelData | ndarray | DataArray | DataFrame | Series | None =None, eval_env: int =1, context: Mapping[str, Any] | None =None) DataFrame[source]¶ Predict values for additional data
- Parameters:¶
- params: ndarray | DataArray | DataFrame | Series¶
Model parameters (nvar by 1)
- exog: PanelData | ndarray | DataArray | DataFrame | Series | None =
None¶ Exogenous regressors (nobs by nvar)
- data: PanelData | ndarray | DataArray | DataFrame | Series | None =
None¶ Values to use when making predictions from a model constructed from a formula
- context: Mapping[str, Any] | None =
None¶ Depth to use when evaluating formulas.
- Returns:¶
Fitted values from supplied data and parameters
- Return type:¶
Notes
If data is not None, then exog must be None. Predictions from models constructed using formulas can be computed using either exog, which will treat these are arrays of values corresponding to the formula-processed data, or using data which will be processed using the formula used to construct the values corresponding to the original model specification.