Factor analysis regression |
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Authors: | Reinhold Kosfeld Jørgen Lauridsen |
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Institution: | (1) Department of Economics, University of Kassel, Nora-Platiel-Str. 5, Kassel, 34117, Germany;(2) Department of Business and Economics, University of Southern Denmark, Campusvej 55, Odense, M 5230, Denmark |
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Abstract: | In the presence of multicollinearity the literature points to principal component regression (PCR) as an estimation method
for the regression coefficients of a multiple regression model. Due to ambiguities in the interpretation, involved by the
orthogonal transformation of the set of explanatory variables, the method could not yet gain wide acceptance. Factor analysis
regression (FAR) provides a model-based estimation method which is particularly tailored to overcome multicollinearity in
an errors-in-variables setting. In this paper two feasible versions of a FAR estimator are compared with the OLS estimator
and the PCR estimator by means of Monte Carlo simulation. While the PCR estimator performs best in cases of strong and high
multicollinearity, the Thomson-based FAR estimator proves to be superior when the regressors are moderately correlated. |
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Keywords: | Factor analysis regression (FAR) Principal component regression (PCR) Multicollinearity Errors-in-variables model |
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