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Spurious Common Factors

Author

Listed:
  • 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.

Suggested Citation

  • Bettina Becker & Stephen G Hall, 2012. "Spurious Common Factors," Discussion Paper Series 2012_12, Department of Economics, Loughborough University, revised Oct 2012.
  • Handle: RePEc:lbo:lbowps:2012_12
    as

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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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    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

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

    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

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