Spurious Common Factors
AbstractWe conduct Monte Carlo simulations of principal components analyses of unrelated time series in order to investigate whether the stationarity properties of the data matter, as they do for least-squares regression analysis. We find that for stationary series the results are standard and reflect the lack of a relationship. For non-stationary series however spurious common factors may persist in large samples.
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Bibliographic InfoPaper provided by Department of Economics, Loughborough University in its series Discussion Paper Series with number 2012_12.
Date of creation: Oct 2012
Date of revision: Oct 2012
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More information through EDIRC
Common factor analysis; Principal components; Spurious regression; Non-stationary data.;
Find related papers by JEL classification:
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Phillips, P.C.B., 1986.
"Understanding spurious regressions in econometrics,"
Journal of Econometrics,
Elsevier, vol. 33(3), pages 311-340, December.
- Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
- Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
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