linearmodels.panel.model.PooledOLS¶

class linearmodels.panel.model.PooledOLS(dependent, exog, *, weights=None, check_rank=True)[source]¶

Pooled coefficient estimator for panel data

Parameters:¶

dependent : array_like¶

Dependent (left-hand-side) variable (time by entity)

exog : array_like¶

Exogenous or right-hand-side variables (variable by time by entity).

weights : array_like¶

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¶

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