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Long- versus medium-run identification in fractionally integrated VAR models

  • Tschernig, Rolf


  • Weber, Enzo


  • Weigand, Roland


We state that long-run restrictions that identify structural shocks in VAR models with unit roots lose their original interpretation if the fractional integration order of the affected variable is below one. For such fractionally integrated models we consider a medium-run approach that employs restrictions on variance contributions over finite horizons. We show for alternative identification schemes that letting the horizon tend to infinity is equivalent to imposing the restriction of Blanchard and Quah (1989) introduced for the unit-root case.

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Paper provided by University of Regensburg, Department of Economics in its series University of Regensburg Working Papers in Business, Economics and Management Information Systems with number 122.

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Date of creation: Feb 2014
Date of revision:
Handle: RePEc:bay:rdwiwi:29408
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  1. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  2. Tschernig, Rolf & Weber, Enzo & Weigand, Roland, 2014. "Long- versus medium-run identification in fractionally integrated VAR models," Economics Letters, Elsevier, vol. 122(2), pages 299-302.
  3. Luis Alberiko Gil-Alana & Antonio Moreno, 2006. "Technology Shocks and Hours Worked: A Fractional Integration Perspective," Faculty Working Papers 03/06, School of Economics and Business Administration, University of Navarra.
  4. Olivier Jean Blanchard & Danny Quah, 1988. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," NBER Working Papers 2737, National Bureau of Economic Research, Inc.
  5. Harald Uhlig, 2004. "Do Technology Shocks Lead to a Fall in Total Hours Worked?," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 361-371, 04/05.
  6. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Long Memory in US Real Output per Capita," CESifo Working Paper Series 2671, CESifo Group Munich.
  7. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2010. "A flexible finite-horizon alternative to long-run restrictions with an application to technology shock," Working Papers 2005-024, Federal Reserve Bank of St. Louis.
  8. Tschernig, Rolf & Weber, Enzo & Weigand, Roland, 2010. "Long-run Identification in a Fractionally Integrated System," University of Regensburg Working Papers in Business, Economics and Management Information Systems 447, University of Regensburg, Department of Economics.
  9. Chung, Ching-Fan, 2001. "Calculating and analyzing impulse responses for the vector ARFIMA model," Economics Letters, Elsevier, vol. 71(1), pages 17-25, April.
  10. Kokoszka, Piotr S. & Taqqu, Murad S., 1995. "Fractional ARIMA with stable innovations," Stochastic Processes and their Applications, Elsevier, vol. 60(1), pages 19-47, November.
  11. Peter C. Schotman & Rolf Tschernig & Jan Budek, 2008. "Long Memory and the Term Structure of Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(4), pages 459-495, Fall.
  12. Gil-Alana, Luis A., 2011. "Inflation in South Africa. A long memory approach," Economics Letters, Elsevier, vol. 111(3), pages 207-209, June.
  13. Johansen, S├śren, 2008. "A Representation Theory For A Class Of Vector Autoregressive Models For Fractional Processes," Econometric Theory, Cambridge University Press, vol. 24(03), pages 651-676, June.
  14. Do, Hung Xuan & Brooks, Robert Darren & Treepongkaruna, Sirimon, 2013. "Generalized impulse response analysis in a fractionally integrated vector autoregressive model," Economics Letters, Elsevier, vol. 118(3), pages 462-465.
  15. Guglielmo Maria Caporale & Luis Gil-Alana, 2011. "Fractional integration and impulse responses: a bivariate application to real output in the USA and four Scandinavian countries," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(1), pages 71-85.
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