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

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  • Tschernig, Rolf
  • Weber, Enzo
  • Weigand, Roland

Abstract

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.

Suggested Citation

  • Tschernig, Rolf & Weber, Enzo & Weigand, Roland, 2013. "Long- versus medium-run identification in fractionally integrated VAR models," University of Regensburg Working Papers in Business, Economics and Management Information Systems 476, University of Regensburg, Department of Economics.
    • Handle: RePEc:bay:rdwiwi:29162
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    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Rolf Tschernig & Enzo Weber & Roland Weigand, 2013. "Long-Run Identification in a Fractionally Integrated System," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 438-450, October.
    3. 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.
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    7. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2014. "A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 638-647, October.
    8. 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.
    9. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    10. Chung, Ching-Fan, 2001. "Calculating and analyzing impulse responses for the vector ARFIMA model," Economics Letters, Elsevier, vol. 71(1), pages 17-25, April.
    11. Peter C. Schotman & Rolf Tschernig & Jan Budek, 2008. "Long Memory and the Term Structure of Risk," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 459-495, Fall.
    12. Johansen, SØren, 2008. "A Representation Theory For A Class Of Vector Autoregressive Models For Fractional Processes," Econometric Theory, Cambridge University Press, vol. 24(3), pages 651-676, June.
    13. 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.
    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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    Cited by:

    1. 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.
    2. Salisu, Afees A. & Ndako, Umar B. & Adediran, Idris A. & Swaray, Raymond, 2020. "A fractional cointegration VAR analysis of Islamic stocks: A global perspective," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    3. Contreras-Reyes, Javier E., 2022. "Rényi entropy and divergence for VARFIMA processes based on characteristic and impulse response functions," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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    More about this item

    Keywords

    Structural vector autoregression; long-run restriction; finite-horizon identification; fractional integration; impulse response function;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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