Principal Components and Factor Analysis. A Comparative Study
AbstractA comparison between Principal Component Analysis (PCA) and Factor Analysis (FA) is performed both theoretically and empirically for a random matrix X:(n x p) , where n is the number of observations and both coordinates may be very large. The comparison surveys the asymptotic properties of the factor scores, of the singular values and of all other elements involved, as well as the characteristics of the methods utilized for detecting the true dimension of X. In particular, the norms of the FA scores, whichever their number, and the norms of their covariance matrix are shown to be always smaller and to decay faster as n goes to infinity. This causes the FA scores, when utilized as regressors and/or instruments, to produce more efficient slope estimators in instrumental variable estimation. Moreover, as compared to PCA, the FA scores and factors exhibit a higher degree of consistency because the difference between the estimated and their true counterparts is smaller, and so is also the corresponding variance. Finally, FA usually selects a much less encumbering number of scores than PCA, greatly facilitating the search and identification of the common components of X.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 35486.
Date of creation: 13 Oct 2011
Date of revision:
Principal Components; Factor Analysis; Matrix Norm;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
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