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On the long-run neutrality of demand shocks

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  • Chen, Wenjuan
  • Netšunajev, Aleksei

Abstract

We revisit the seminal paper by Blanchard and Quah (1989) and investigate their long-run identification scheme. We use a structural VAR model with smoothly changing covariances for the identification of shocks. Formal testing rejects the long-run neutrality of demand shocks.

Suggested Citation

  • Chen, Wenjuan & Netšunajev, Aleksei, 2016. "On the long-run neutrality of demand shocks," Economics Letters, Elsevier, vol. 139(C), pages 57-60.
  • Handle: RePEc:eee:ecolet:v:139:y:2016:i:c:p:57-60 DOI: 10.1016/j.econlet.2015.11.039
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    References listed on IDEAS

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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. Baxter, Marianne & King, Robert G, 1993. "Fiscal Policy in General Equilibrium," American Economic Review, American Economic Association, vol. 83(3), pages 315-334, June.
    3. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    4. Dornbusch, Rudiger & Frenkel, Jacob A, 1973. "Inflation and Growth: Alternative Approaches," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 5(1), pages 141-156, Part I Fe.
    5. Emanuele Bacchiocchi & Luca Fanelli, 2015. "Identification in Structural Vector Autoregressive Models with Structural Changes, with an Application to US Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(6), pages 761-779, December.
    6. Helmut Lütkepohl & Aleksei Netsunajev, 2014. "Structural Vector Autoregressions with Smooth Transition in Variances - The Interaction Between U.S. Monetary Policy and the Stock Market," SFB 649 Discussion Papers SFB649DP2014-031, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, pages 1379-1399.
    8. John W. Keating, 2013. "What Do We Learn from Blanchard and Quah Decompositions If Aggregate Demand May Not be Long-Run Neutral?," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201302, University of Kansas, Department of Economics.
    9. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    10. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    11. Keating, John W., 2013. "What do we learn from Blanchard and Quah decompositions of output if aggregate demand may not be long-run neutral?," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 203-217.
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    Cited by:

    1. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    2. Dieter Nautz & Aleksei Netsunajev & Till Strohsal, 2016. "Aggregate Employment, Job Polarization and Inequalities: A Transatlantic Perspective," SFB 649 Discussion Papers SFB649DP2016-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Nautz, Dieter & Netsunajew, Aleksei & Strohsal, Till, 2017. "The Anchoring of Inflation Expectations in the Short and in the Long Run," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168075, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    Smooth transition VAR models; Identification via heteroskedasticity; Long-run neutrality;

    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

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