In presence of multicollinearity principal component regression (PCR) is sometimes suggested for the estimation of 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 particular tailored to overcome multicollinearity in an errors in variables setting. In this paper we present a new FAR estimator that proves to be unbiased and consistent for the coefficient vector of a multiple regression model given the parameters of the measurement model. The behaviour of feasible FAR estimators in the general case of completely unknown model parameters is studied in comparison with the OLS estimator by means of Monte Carlo simulation.
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Paper provided by University of Kassel, Institute of Economics in its series Discussion Papers in Economics with number
57/04.
Length: 20 pages Date of creation: May 2004 Date of revision: Publication status: Published in Statistical Papers (2006) online Handle: RePEc:kas:wpaper:2004-57
Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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