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Spurious Regressions of Stationary AR(p) Processes with Structural Breaks

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  • Chu Ba

    () (Carleton University)

  • Kozhan Roman

    () (University of Warwick)

Abstract

When a pair of independent series is highly persistent, there is a spurious regression bias in a regression between these series, closely related to the classic studies of Granger and Newbold (1974). Although this is well known to occur with independent I(1) processes, this paper provides theoretical and numerical evidence that the phenomenon of spurious regression also arises in regressions between stationary AR(p) processes with structural breaks, which occur at different points in time, in the means and the trends. The intuition behind this is that structural breaks can increase the persistence levels in the processes (e.g., Granger and Hyung (2004)), which then leads to spurious regressions. These phenomena occur for general distributions and serial dependence of the innovation terms.

Suggested Citation

  • Chu Ba & Kozhan Roman, 2010. "Spurious Regressions of Stationary AR(p) Processes with Structural Breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(1), pages 1-25, December.
  • Handle: RePEc:bpj:sndecm:v:15:y:2010:i:1:n:1
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    Cited by:

    1. repec:wly:japmet:v:31:y:2016:i:7:p:1467-1477 is not listed on IDEAS
    2. Gerdie Everaert & Hauke Vierke, 2016. "Demographics and Business Cycle Volatility: A Spurious Relationship?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1467-1477, November.

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