Factor Analysis Regression
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper 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
Factor Analysis Regression; Multicollinearity; Factor model; Errors in Variables;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jochen Michaelis).
If references are entirely missing, you can add them using this form.