# linearmodels.panel.model.RandomEffects¶

class RandomEffects(dependent, exog, *, weights=None)[source]

One-way Random Effects model 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.

Notes

The model is given by

$y_{it} = \beta^{\prime}x_{it} + u_i + \epsilon_{it}$

where $$u_i$$ is a shock that is independent of $$x_{it}$$ but common to all entities i.

Methods

 fit(self, \*, small_sample, cov_type, …) from_formula(formula, data, numpy.ndarray, …) Create a model from a formula predict(self, params, …) Predict values for additional data reformat_clusters(self, 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