# Implementation Choices¶

While the implementation of the panel estimators is similar to Stata, there are some differenced worth noting.

## Clustered Covariance with Fixed Effects¶

When using clustered standard errors and entity effects, it is not necessary
to adjust for estimated effects. `PanelOLS`

attempts to detect when this is
the case and automatically adjust the degree of freedom. This can be
overridden using by setting the fit option `auto_df=False`

and then
changing the value of `count_effects`

.

## \(R^2\) definitions¶

The \(R^2\) definitions are all designed so that the reported value will
match the original model using the estimated parameters. This differs from
other packages, such as Stata, which use a correlation based measure which
ignores the estimated intercept (if included) and allows for affine
adjustments to estimated parameters. The main reported \(R^2\)
(`rsquared`

in returned results) is always the \(R^2\) from
the actual model fit, after adjusting the data for:

weights (all estimators)

effects (

`PanelOLS`

)re-centering (

`RandomEffects`

)within entity aggregation (

`BetweenOLS`

)differencing (

`FirstDifferenceOLS`

)