linearmodels.panel.model.PanelOLS¶

One- and two-way fixed effects 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.
entity_effects: bool = False¶: Flag whether to include entity (fixed) effects in the model
time_effects: bool = False¶: Flag whether to include time effects in the model
other_effects: PanelData | ndarray | DataArray | DataFrame | Series | None = None¶: Category codes to use for any effects that are not entity or time effects. Each variable is treated as an effect.
singletons: bool = True¶: Flag indicating whether to drop singleton observation
drop_absorbed: bool = False¶: Flag indicating whether to drop absorbed variables
check_rank: bool = True¶: 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

Many models can be estimated. The most common included entity effects and can be described

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

where \(\alpha_i\) is included if entity_effects=True.

Time effect are also supported, which leads to a model of the form

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

where \(\gamma_i\) is included if time_effects=True.

Both effects can be simultaneously used,

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

Additionally , arbitrary effects can be specified using categorical variables.

If both entity_effect and time_effects are False, and no other effects are included, the model reduces to PooledOLS.

Model supports at most 2 effects. These can be entity-time, entity-other, time-other or 2 other.

Methods

`fit`(*[, use_lsdv, use_lsmr, low_memory, ...])	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

`entity_effects`	Flag indicating whether entity effects are included
`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
`other_effects`	Flag indicating whether other (generic) effects are included
`time_effects`	Flag indicating whether time effects are included