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)