IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb649/sfb649dp2015-043.html
   My bibliography  Save this paper

On the long-run neutrality of demand shocks

Author

Listed:
  • Chen, Wenjuan
  • Netsunajev, Aleksei

Abstract

Long run neutrality restrictions have been widely used to identify structural shocks in VAR models. This paper revisits the seminal paper by Blanchard and Quah (1989), and investigates their identification scheme. We use structural VAR models with smoothly changing covariances for identification of shocks. The resulted impulse responses are economically meaningful. Formal test results reject the long-run neutrality of demand shocks.

Suggested Citation

  • Chen, Wenjuan & Netsunajev, Aleksei, 2015. "On the long-run neutrality of demand shocks," SFB 649 Discussion Papers 2015-043, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Handle: RePEc:zbw:sfb649:sfb649dp2015-043
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/122012/1/837591554.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. Markku Lanne & Helmut Lütkepohl, 2008. "Identifying Monetary Policy Shocks via Changes in Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1131-1149, September.
    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. Lütkepohl, Helmut & Netésunajev, Aleksei, 2014. "Structural vector autoregressions with smooth transition in variances: The interaction between US monetary policy and the stock market," SFB 649 Discussion Papers 2014-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. 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, vol. 52(8), pages 1379-1399, November.
    7. 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.
    8. Baxter, Marianne & King, Robert G, 1993. "Fiscal Policy in General Equilibrium," American Economic Review, American Economic Association, vol. 83(3), pages 315-334, June.
    9. 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.
    10. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nautz, Dieter & Strohsal, Till & Netšunajev, Aleksei, 2019. "The Anchoring Of Inflation Expectations In The Short And In The Long Run," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1959-1977, July.
    2. 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.
    3. Helmut Lütkepohl & Mika Meitz & Aleksei Netšunajev & Pentti Saikkonen, 2021. "Testing identification via heteroskedasticity in structural vector autoregressive models," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 1-22.
    4. repec:hum:wpaper:sfb649dp2016-015 is not listed on IDEAS
    5. Ngomba Bodi, Francis Ghislain, 2018. "Contributions relatives des chocs de demande agrégée et d’offre agrégée aux fluctuations de la croissance réelle en zone CEMAC [Relative contributions of aggregate demand and supply shocks to busin," MPRA Paper 116376, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:hum:wpaper:sfb649dp2015-043 is not listed on IDEAS
    2. Netsunajev, Aleksei, 2013. "Reaction to technology shocks in Markov-switching structural VARs: Identification via heteroskedasticity," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 51-62.
    3. 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.
    4. Campos, Luciano & Casas, Agustín, 2021. "Rara Avis: Latin American populism in the 21st century," European Journal of Political Economy, Elsevier, vol. 70(C).
    5. Lanne, Markku & Meitz, Mika & Saikkonen, Pentti, 2017. "Identification and estimation of non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 196(2), pages 288-304.
    6. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    7. Dominik Bertsche & Robin Braun, 2022. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 328-341, January.
    8. Ambler, Steve & Guay, Alain & Phaneuf, Louis, 2012. "Endogenous business cycle propagation and the persistence problem: The role of labor-market frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 47-62.
    9. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2020. "Proxy SVAR identification of monetary policy shocks: MonteCarlo evidence and insights for the US," University of Göttingen Working Papers in Economics 404, University of Goettingen, Department of Economics.
    10. Herwartz, Helmut & Lange, Alexander & Maxand, Simone, 2019. "Statistical identification in SVARs - Monte Carlo experiments and a comparative assessment of the role of economic uncertainties for the US business cycle," University of Göttingen Working Papers in Economics 375, University of Goettingen, Department of Economics.
    11. Demetrescu, Matei & Salish, Nazarii, 2024. "(Structural) VAR models with ignored changes in mean and volatility," International Journal of Forecasting, Elsevier, vol. 40(2), pages 840-854.
    12. 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, vol. 52(8), pages 1379-1399, November.
    13. Dungey, Mardi & Milunovich, George & Thorp, Susan & Yang, Minxian, 2015. "Endogenous crisis dating and contagion using smooth transition structural GARCH," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 71-79.
    14. Pu Chen, Armon Rezai, Willi Semmler, 2007. "WP 2007-8 Productivity and Unemployment in the Short and Long Run," SCEPA working paper series. 2007-8, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    15. Tomislav Globan, 2015. "Financial integration, push factors and volatility of capital flows: evidence from EU new member states," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 643-672, August.
    16. Giancarlo Corsetti & Luca Dedola & Sylvain Leduc, 2008. "Productivity, External Balance, and Exchange Rates: Evidence on the Transmission Mechanism among G7 Countries," NBER Chapters, in: NBER International Seminar on Macroeconomics 2006, pages 117-194, National Bureau of Economic Research, Inc.
    17. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    18. Pu Chen & Willi Semmler, 2018. "Short and Long Effects of Productivity on Unemployment," Open Economies Review, Springer, vol. 29(4), pages 853-878, September.
    19. Lütkepohl, Helmut & Netšunajev, Aleksei, 2015. "Structural vector autoregressions with heteroskedasticity: A comparison of different volatility models," SFB 649 Discussion Papers 2015-015, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    20. Emanuele BACCHIOCCHI & Riccardo Jack LUCCHETTI, 2015. "Structure-Based SVAR Identification," Departmental Working Papers 2015-11, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    21. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).

    More about this item

    Keywords

    smooth transition VAR models; identification via heteroskedasticity; long-run neutrality; aggregate demand; aggregate supply;
    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:sfb649:sfb649dp2015-043. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sohubde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.