linearmodels.panel.model.PooledOLS¶

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`	Formula used to construct the model
`has_constant`	Flag indicating the model a constant or implicit constant
`not_null`	Locations of non-missing observations