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Granger-Causality in the presence of structural breaks

  • Daniel Ventosa-Santaulària

    ()

    (Universidad de Guanajuato)

  • José Eduardo Vera-Valdés

    ()

    (Universidad de Guanajuato)

The concept of Granger-Causality (GC) is widely used to draw inference concerning causality in applied economics. Stationary series are the term of reference used in GC testing, which is generally studied by means of a standard Dickey-Fuller test. We prove that, when the Data Generating Process (DGP) of the variables is either Broken-Trend Stationary (BTS) or Broken-Mean Stationary (BMS), correct inference can not be drawn from a standard Granger-Causality test and may identify inexistent causal relationships, even if the former variables are differenced. Asymptotic and finite-sample evidence in this sense is provided.

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Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 3 (2008)
Issue (Month): 61 ()
Pages: 1-14

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Handle: RePEc:ebl:ecbull:eb-08c20013
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  9. Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
  10. Ventosa-Santaularària, Daniel & Gómez, Manuel, 2006. "Inflation and Breaks: the validity of the Dickey-Fuller test," MPRA Paper 58773, University Library of Munich, Germany.
  11. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  12. Dufour, J.M. & Renault, E., 1995. "Short-Run and Long-Rub Causality in Time Series: Theory," Cahiers de recherche 9538, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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