Spurious Common Factors
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.
|Date of creation:||Oct 2012|
|Date of revision:||Oct 2012|
|Contact details of provider:|| 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
More information through EDIRC
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.:
- Peter C.B. Phillips, 1985.
"Understanding Spurious Regressions in Econometrics,"
Cowles Foundation Discussion Papers
757, Cowles Foundation for Research in Economics, Yale University.
- Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
- Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
When requesting a correction, please mention this item's handle: RePEc:lbo:lbowps:2012_12. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Huw Edwards)
If references are entirely missing, you can add them using this form.