linearmodels.panel.model.PooledOLS¶
-
class linearmodels.panel.model.PooledOLS(dependent: PanelData | ndarray | DataArray | DataFrame | Series, exog: PanelData | ndarray | DataArray | DataFrame | Series, *, weights: PanelData | ndarray | DataArray | DataFrame | Series | None =
None
, check_rank: bool =True
)[source]¶ Pooled coefficient estimator for panel data
- Parameters:¶
- dependent: PanelData | ndarray | DataArray | DataFrame | Series¶
Dependent (left-hand-side) variable (time by entity)
- exog: PanelData | ndarray | DataArray | DataFrame | Series¶
Exogenous or right-hand-side variables (variable by time by entity).
- weights: PanelData | ndarray | DataArray | DataFrame | Series | None =
None
¶ Weights to use in estimation. Assumes residual variance is proportional to inverse of weight to that the residual time the weight should be homoskedastic.
- check_rank: bool =
True
¶ Flag indicating whether to perform a rank check on the exogenous variables to ensure that the model is identified. Skipping this check can reduce the time required to validate a model specification. Results may be numerically unstable if this check is skipped and the matrix is not full rank.
Notes
The model is given by
\[y_{it}=\beta^{\prime}x_{it}+\epsilon_{it}\]Methods
fit
(*[, cov_type, debiased])Estimate model parameters
from_formula
(formula, data, *[, weights, ...])Create a model from a formula
predict
(params, *[, exog, data, eval_env, ...])Predict values for additional data
reformat_clusters
(clusters)Reformat cluster variables
Properties
Formula used to construct the model
Flag indicating the model a constant or implicit constant
Locations of non-missing observations