Date of Completion
2025
Thesis Type
College of Arts and Science Honors
Department
Economics
First Advisor
Richard Sicotte
Keywords
econometrics, frisch-waugh-lovell, ordinary least squares, omitted variable bias, misspecification
Abstract
The Frisch-Waugh-Lovell (FWL) theorem is a foundational result in the interpretation of regression coefficients estimated by ordinary least squares, and is ubiquitous in econometrics education. In this thesis I present two recursive FWL algorithms, implement both in Python, and study their applications in representing bias with multiple omitted variables. I find that one of the algorithms, recursive FWL decompositions, can be used to obtain an expression of bias with multiple omitted variables that is equivalent to the standard result. By breaking the omitted variable bias result into smaller terms that then simplify into the standard result, the recursive decompositions approach provides a more granular perspective on the form of omitted variable bias.
Recommended Citation
Tiffen, Rae, "Two Recursive Frisch-Waugh-Lovell Algorithms and Applications in Representing Bias with Multiple Omitted Variables" (2025). UVM College of Arts and Sciences College Honors Theses. 161.
https://scholarworks.uvm.edu/castheses/161