The Regression with Orthogonal Variables and the Raise Regression in the STIRPAT Model
DOI:
https://doi.org/10.25115/eea.v35i3.2504Keywords:
STIRPAT, Collinearity, Raise Regression, Regression With Orthogonal Variables.Abstract
STIRPAT model is one of the most analyzed methodologies in environmental studies. The specification of this econometric model provokes multicollinearity. Although a first option could be to eliminate the variable (or variables) that generates the collinearity, it does not allow to estimate the effects of the main forces driving environmental impacts. It is necessary to develop or to apply new methods that could mitigate the collinearity problem in the STIRPAT model. This work applies two methodologies alternatives to the traditional Ordinary Least Squares (OLS) estimation: the raise regression and the regression with orthogonal variables. Both methodologies manage to mitigate the collinearity between variables that exists in the original model, and furthermore, they have two different perspectives about the variables: while the raised method has an important geometric factor in its application, the purpose of the regression with orthogonal variables is to give new interpretations of the variables.Downloads
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