linearmodels.panel.model.RandomEffects¶

One-way Random Effects model 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.

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`(*[, small_sample, 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