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Spurious Common Factors

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Author Info

  • Bettina Becker

    (School of Business and Economics, Loughborough University, UK)

  • Stephen G Hall

    (Department of Economics, University of Leicester, UK)

Abstract

We 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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File URL: http://www.lboro.ac.uk/departments/sbe/RePEc/lbo/lbowps/Becker_Hall_DP_2012_12.pdf
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Bibliographic Info

Paper provided by Department of Economics, Loughborough University in its series Discussion Paper Series with number 2012_12.

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Date of creation: Oct 2012
Date of revision: Oct 2012
Handle: RePEc:lbo:lbowps:2012_12

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Postal: Loughborough, Leicestershire, LE11 3TU
Phone: +44 (0) 1509 222701
Fax: +44 (0) 1509 223910
Web page: http://www.lboro.ac.uk/departments/sbe/research/economics/index.html
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Related research

Keywords: Common factor analysis; Principal components; Spurious regression; Non-stationary data.;

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  1. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
  2. 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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