Generalization of the residualization procedure. Properties and environmental applications
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García García, ClaudiaEditorial
Universidad de Granada
Departamento
Universidad de Granada. Programa de Doctorado en Ciencias Económicas y EmpresarialesMateria
Multicollinearity Environmental applications Econometric models Environmental economics Multicolinealidad Aplicaciones medioambientales Modelos econométricos Economía ambiental
Date
2020Fecha lectura
2020-07-23Referencia bibliográfica
García García, Claudia. Generalization of the residualization procedure. Properties and environmental applications. Granada: Universidad de Granada, 2020. [http://hdl.handle.net/10481/63333]
Sponsorship
Tesis Univ. Granada.Abstract
The dissertation presented here aims to clarify the role of multicollinearity in
an econometric model and proposes residualization as a good methodology not
only to deal with the problem but also to achieve another type of interpretation
of the explanatory variables from the model under consideration.
Chapter 1 gave the reader an initial introduction to the problem and oered
a brief explanation of the methodology being presented. Chapter 2 then looked
in depth at the multicollinearity problem and the traditional methodologies
used in this eld. Chapters 3 and 4 explain the methodology further; Chapter
3 focuses its attention on earlier works in the eld: criticism of the method and
methodological preliminaries, while Chapter 4 presents the generalization of
the method, together with the justication and properties of the residualization
procedure. These two chapters and in particular Chapter 4, are the main
contribution of this Thesis. Finally, Chapter 5 presents the empirical part: three
specic models on environmental economics, which present strong collinearity problems.
In conclusion, even when the goal of the study is to predict (where it has been concluded previously that collinearity is not signicant), it is highly
recommended to mitigate the problem because the researcher needs to be very
sure of the continuity of the relationships between explanatory variables in the
future because, if the relationship changes, the forecast based on the initial
model may be unreliable as well.