Quantitative Methods for a Linear Regression Model with Multicollinearity. Application to Yields of Treasury Bills

Authors

  • Román Salmerón Gómez Departamento de Métodos Cuantitativos para la Economía y la Empresa Universidad de Granada
  • Eduardo Rodríguez Martínez Máster en Técnicas Cuantitativas en Gestión Empresarial Universidad de Granada

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.2886

Keywords:

modelos de regresión, multicolinealidad, regresión alzada, regresión cresta, regresión con variables ortogonales, regression models, multicollinearity, raised regression, ridge regression, regression with orthogonal variables

Abstract

It is known that, when in the linear regression model there is a high degree of multicollinearity, the results obtained by using the Ordinary Least Squares (OLS) method are unstable. As a solution to this situation, in this paper we present the raised method, the ridge method and the orthogonal variables method as an alternative to the estimate by OLS. It is also shown that regression with orthogonal variables makes sense regardless of the existence of serious multicollinearity because it allows to answer questions which are not accessible when using the original model. These methodologies are applied to a data set of yields of treasury bills.

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References

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Published

2017-12-20

How to Cite

Salmerón Gómez, R., & Rodríguez Martínez, E. (2017). Quantitative Methods for a Linear Regression Model with Multicollinearity. Application to Yields of Treasury Bills. Journal of Quantitative Methods for Economics and Business Administration, 24, Páginas 169 a 189. https://doi.org/10.46661/revmetodoscuanteconempresa.2886

Issue

Vol. 24 (2017)

Section

Articles

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Copyright (c) 2017 Journal of Quantitative Methods for Economics and Business Administration

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