wls() takes an OLS model and re-estimates it using a weighted least squares
approach. Weighted least squares is often a "textbook" approach to dealing with the presence of
heteroskedastic standard errors, for which the weighted least squares estimates are compared
to the OLS estimates of uncertainty to check for consistency or potential inferential implications.
Usage
wls(mod)
Value
wls() returns a new model object that is a weighted least squares re-estimation
of the OLS model supplied to it.
Arguments
mod
a fitted OLS model
Author
Steven V. Miller
Details
The function should be robust to potential model specification oddities (e.g. polynomials
and fixed effects). It also should perform nicely in the presence of missing data, if and only
if na.action = na.exclude is supplied first to the offending OLS model supplied to the function
for a weighted least squares re-estimation.