linearmodels.panel.model.PooledOLS

class PooledOLS(dependent, exog, *, weights=None, check_rank=True)[source]

Pooled coefficient estimator for panel data

Parameters
dependentarray_like

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

exogarray_like

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

weightsarray_like, optional

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_rankbool, optional

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

\[y_{it}=\beta^{\prime}x_{it}+\epsilon_{it}\]
Attributes
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

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

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