RT Book, Section
T1 Multicollinearity mitigation and unbiased estimations: an application of restricted least squares
A1 Salmerón Gómez, Román
A1 García García, Claudia
A1 García-García, Catalina B.
A2 Cruz Rambaud, Salvador
A2 Trinidad Segovia, Juan Evangelista
A2 García-García, Catalina B.
AB This work presents an application of Restricted Least Squares (RLS) that achieves the mitigation of severe multicollinearity problems along with obtaining unbiased estimates. Both targets are restricted to the verification of the null hypothesis of the restrictions on the coefficients of the RLS model, which has been obtained with the application of two recent methodologies that mitigate multicollinearity problems: residualization and raise regression. The reliability of the proposed methodology is shown through Monte Carlo simulations, which illustrate that it is independent of the degree and type of multicollinearity. Finally, a particular empirical application is implemented, and it displays the possible combination of constraints depending on the case study. In conclusion, this application introduces a technique that can be used in all fields and particular studies where problematic relationships among explanatory variables emerge and the traditional methodologies to overcome the issue are biased techniques.
PB Springer
SN 978-3-031-84782-0
SN 978-3-031-84781-3
YR 2025
FD 2025
LK https://hdl.handle.net/20.500.14352/125585
UL https://hdl.handle.net/20.500.14352/125585
LA eng
NO Salmerón Gómez, R.; García-García, C., & García-García, C.B. (2025). Multicollinearity Mitigation and Unbiased Estimations: An Application of Restricted Least Squares. En: Advances in Quantitative Methods for Economics and Business (dirs. Cruz Rambaud, S.; Trinidad Segovia, J.E. y García, C.B). Springer Nature.
NO Junta de Andalucía
DS Docta Complutense
RD 5 nov 2025