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
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: