Using Feasible Generalized Least Squares To Improve Estimates
Data analysis in reality is rarely as clean and tidy as it is presented in the textbooks. Consider linear regression — data rarely meets the stringent assumptions required for OLS. Failing to recognize this and incorrectly implementing OLS can lead to embarrassing, inaccurate conclusions.
In today’s blog, we’ll look at how to use feasible generalized least squares to deal with data that does not meet the OLS assumption of Independent and Identically Distributed (IID) error terms.