linearmodels.panel.model.FamaMacBeth¶

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.

Notes

The model is given by

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

The Fama-MacBeth estimator is computed by performing T regressions, one for each time period using all available entity observations. Denote the estimate of the model parameters as \(\hat{\beta}_t\). The reported estimator is then

\[\hat{\beta} = T^{-1}\sum_{t=1}^T \hat{\beta}_t\]

While the model does not explicitly include time-effects, the implementation based on regressing all observation in a single time period is “as-if” time effects are included.

Parameter inference is made using the set of T parameter estimates with either the standard covariance estimator or a kernel-based covariance, depending on cov_type.

Methods

`fit`([cov_type, debiased, bandwidth, kernel])	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