IDEAS home Printed from https://ideas.repec.org/p/hum/wpaper/sfb649dp2014-009.html
   My bibliography  Save this paper

Structural Vector Autoregressions: Checking Identifying Long-run Restrictions via Heteroskedasticity

Author

Listed:
  • Helmut Lütkepohl
  • Anton Velinov

Abstract

Long-run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just-identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have been used for this purpose. Three main approaches have been used, exogenously generated changes in the unconditional residual covariance matrix, changing volatility modelled by a Markov switching mechanism and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. Using changes in volatility for checking long-run identifying restrictions in structural VAR analysis is illustrated by reconsidering models for identifying fundamental components of stock prices.

Suggested Citation

  • Helmut Lütkepohl & Anton Velinov, 2014. "Structural Vector Autoregressions: Checking Identifying Long-run Restrictions via Heteroskedasticity," SFB 649 Discussion Papers SFB649DP2014-009, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2014-009
    as

    Download full text from publisher

    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2014-009.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. Hansen, Bruce E, 1992. "The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 61-82, Suppl. De.
    3. Helmut Lütkepohl, 2013. "Reducing confidence bands for simulated impulse responses," Statistical Papers, Springer, vol. 54(4), pages 1131-1145, November.
    4. 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.
    5. 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.
    6. Rapach, David E., 2001. "Macro shocks and real stock prices," Journal of Economics and Business, Elsevier, vol. 53(1), pages 5-26.
    7. Gonzalo, Jesus & Ng, Serena, 2001. "A systematic framework for analyzing the dynamic effects of permanent and transitory shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 25(10), pages 1527-1546, October.
    8. Weide, R. van der, 2002. "Generalized Orthogonal GARCH. A Multivariate GARCH model," CeNDEF Working Papers 02-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    9. Till Strohsal & Enzo Weber, 2012. "The Signal of Volatility," SFB 649 Discussion Papers SFB649DP2012-043, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2004. "The Response of Hours to a Technology Shock: Evidence Based on Direct Measures of Technology," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 381-395, 04/05.
    11. Anton Velinov, 2013. "Can Stock Price Fundamentals Properly be Captured?: Using Markov Switching in Heteroskedasticity Models to Test Identification Schemes," Discussion Papers of DIW Berlin 1350, DIW Berlin, German Institute for Economic Research.
    12. Michael Ehrmann & Marcel Fratzscher & Roberto Rigobon, 2011. "Stocks, bonds, money markets and exchange rates: measuring international financial transmission," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 948-974, September.
    13. Pagan, A.R. & Pesaran, M. Hashem, 2008. "Econometric analysis of structural systems with permanent and transitory shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3376-3395, October.
    14. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
    15. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    16. Markku Lanne & Helmut Luetkepohl, 2008. "A Statistical Comparison of Alternative Identification Schemes for Monetary Policy Shocks," Economics Working Papers ECO2008/23, European University Institute.
    17. Bjørnland, Hilde C. & Leitemo, Kai, 2009. "Identifying the interdependence between US monetary policy and the stock market," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 275-282, March.
    18. Laopodis, Nikiforos T., 2009. "Are fundamentals still relevant for European economies in the post-Euro period?," Economic Modelling, Elsevier, vol. 26(5), pages 835-850, September.
    19. Enzo Weber, 2010. "Structural Conditional Correlation," Journal of Financial Econometrics, Oxford University Press, vol. 8(3), pages 392-407, Summer.
    20. Roberto Rigobon & Brian Sack, 2003. "Measuring The Reaction of Monetary Policy to the Stock Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(2), pages 639-669.
    21. Normandin, Michel & Phaneuf, Louis, 2004. "Monetary policy shocks:: Testing identification conditions under time-varying conditional volatility," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1217-1243, September.
    22. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    23. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    24. Rigobon, Roberto & Sack, Brian, 2004. "The impact of monetary policy on asset prices," Journal of Monetary Economics, Elsevier, vol. 51(8), pages 1553-1575, November.
    25. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    26. Lance A. Fisher & Hyeon-seung Huh, 2014. "Identification Methods In Vector-Error Correction Models: Equivalence Results," Journal of Economic Surveys, Wiley Blackwell, vol. 28(1), pages 1-16, February.
    27. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    28. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    29. Bouakez, Hafedh & Normandin, Michel, 2010. "Fluctuations in the foreign exchange market: How important are monetary policy shocks?," Journal of International Economics, Elsevier, vol. 81(1), pages 139-153, May.
    30. Lee, Bong-Soo, 1996. "Comovements of earnings, dividends, and stock prices," Journal of Empirical Finance, Elsevier, vol. 3(4), pages 327-346, December.
    31. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    32. I. D. Vrontos & P. Dellaportas & D. N. Politis, 2003. "A full-factor multivariate GARCH model," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 312-334, December.
    33. Herwartz, Helmut & Lütkepohl, Helmut, 2014. "Structural vector autoregressions with Markov switching: Combining conventional with statistical identification of shocks," Journal of Econometrics, Elsevier, vol. 183(1), pages 104-116.
    34. Roy van der Weide, 2002. "GO-GARCH: a multivariate generalized orthogonal GARCH model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 549-564.
    35. Sentana, Enrique & Fiorentini, Gabriele, 2001. "Identification, estimation and testing of conditionally heteroskedastic factor models," Journal of Econometrics, Elsevier, vol. 102(2), pages 143-164, June.
    36. Helmut Lütkepohl & Aleksei NetŠunajev, 2014. "Disentangling Demand And Supply Shocks In The Crude Oil Market: How To Check Sign Restrictions In Structural Vars," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 479-496, April.
    37. Fisher, Lance A. & Huh, Hyeon-Seung & Summers, Peter M., 2000. "Structural Identification of Permanent Shocks in VEC Models: A Generalization," Journal of Macroeconomics, Elsevier, vol. 22(1), pages 53-68, January.
    38. Garcia, Rene, 1998. "Asymptotic Null Distribution of the Likelihood Ratio Test in Markov Switching Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 763-788, August.
    39. Zacharias Psaradakis & Nicola Spagnolo, 2003. "On The Determination Of The Number Of Regimes In Markov‐Switching Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 237-252, March.
    40. Emanuele BACCHIOCCHI & Luca FANELLI, 2012. "Identification in structural vector autoregressive models with structural changes," Departmental Working Papers 2012-16, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    41. Binswanger, Mathias, 2004. "How do stock prices respond to fundamental shocks?," Finance Research Letters, Elsevier, vol. 1(2), pages 90-99, June.
    42. Jean Louis, Rosmy & Eldomiaty, Tarek, 2010. "How do stock prices respond to fundamental shocks in the case of the United States? Evidence from NASDAQ and DJIA," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 310-322, August.
    43. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    44. Zacharias Psaradakis & Nicola Spagnolo, 2006. "Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 753-766, September.
    45. Juan F. Rubio-Ramirez & Daniel F. Waggoner & Tao Zha, 2005. "Markov-switching structural vector autoregressions: theory and application," FRB Atlanta Working Paper 2005-27, Federal Reserve Bank of Atlanta.
    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. 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. Lütkepohl, Helmut & Woźniak, Tomasz, 2020. "Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    3. Nestor Azcona, 2017. "Non-Traded Goods and Real Exchange Rate Fluctuations: A Structural VAR Analysis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 23(2), pages 137-148, May.
    4. Helmut Lütkepohl & Thore Schlaak, 2018. "Choosing Between Different Time‐Varying Volatility Models for Structural Vector Autoregressive Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 715-735, August.
    5. Arefiev, Nikolay & Khabibullin, Ramis, 2018. "Bayesian identification of structural vector autoregression models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 49, pages 115-142.
    6. Helmut Lütkepohl & Aleksei Netšunajev, 2018. "The Relation between Monetary Policy and the Stock Market in Europe," Econometrics, MDPI, vol. 6(3), pages 1-14, August.
    7. 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.
    8. Fatih Chellai, 2021. "What can SVAR models tell us about the impact of Public Expenditure Shocks on macroeconomic variables in algeria? A Slight Hint to the COVID-19 Pandemic," Folia Oeconomica Stetinensia, Sciendo, vol. 21(2), pages 21-37, December.
    9. Velinov, Anton & Chen, Wenjuan, 2015. "Do stock prices reflect their fundamentals? New evidence in the aftermath of the financial crisis," Journal of Economics and Business, Elsevier, vol. 80(C), pages 1-20.
    10. Puonti, Päivi, 2016. "Fiscal multipliers in a structural VEC model with mixed normal errors," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 144-154.
    11. Anton Velinov & Wenjuan Chen, 2014. "Are There Bubbles in Stock Prices?: Testing for Fundamental Shocks," Discussion Papers of DIW Berlin 1375, DIW Berlin, German Institute for Economic Research.

    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. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticity - A Comparison of Different Volatility Models," CESifo Working Paper Series 5308, CESifo.
    2. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticy," SFB 649 Discussion Papers SFB649DP2015-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    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. Helmut Lütkepohl, 2012. "Identifying Structural Vector Autoregressions via Changes in Volatility," Discussion Papers of DIW Berlin 1259, DIW Berlin, German Institute for Economic Research.
    5. Skrobotov, Anton (Скроботов, Антон) & Turuntseva, Marina (Турунцева, Марина), 2015. "Theoretical Aspects of Modeling of the SVAR [Теоретические Аспекты Моделирования Svar]," Published Papers mak8, Russian Presidential Academy of National Economy and Public Administration.
    6. Herwartz, Helmut & Lütkepohl, Helmut, 2014. "Structural vector autoregressions with Markov switching: Combining conventional with statistical identification of shocks," Journal of Econometrics, Elsevier, vol. 183(1), pages 104-116.
    7. Helmut Lütkepohl & Aleksei Netsunajev, 2014. "Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market," Discussion Papers of DIW Berlin 1388, DIW Berlin, German Institute for Economic Research.
    8. 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.
    9. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    10. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with smooth transition in variances," Journal of Economic Dynamics and Control, Elsevier, vol. 84(C), pages 43-57.
    11. Dmitry Kulikov & Aleksei Netsunajev, 2016. "Identifying Shocks in Structural VAR models via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2015-8, Bank of Estonia, revised 19 Feb 2016.
    12. Helmut Lütkepohl & Aleksei Netšunajev, 2018. "The Relation between Monetary Policy and the Stock Market in Europe," Econometrics, MDPI, vol. 6(3), pages 1-14, August.
    13. Helmut Lütkepohl & Aleksei NetŠunajev, 2014. "Disentangling Demand And Supply Shocks In The Crude Oil Market: How To Check Sign Restrictions In Structural Vars," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 479-496, April.
    14. Velinov, Anton, 2016. "On the importance of testing structural identification schemes and the potential consequences of incorrectly identified models," VfS Annual Conference 2016 (Augsburg): Demographic Change 145581, Verein für Socialpolitik / German Economic Association.
    15. 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.
    16. Velinov, Anton & Chen, Wenjuan, 2015. "Do stock prices reflect their fundamentals? New evidence in the aftermath of the financial crisis," Journal of Economics and Business, Elsevier, vol. 80(C), pages 1-20.
    17. Helmut Lütkepohl & Thore Schlaak, 2018. "Choosing Between Different Time‐Varying Volatility Models for Structural Vector Autoregressive Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 715-735, August.
    18. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    19. 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.
    20. Emanuele Bacchiocchi & Efrem Castelnuovo & Luca Fanelli, 2014. "Gimme a break! Identification and estimation of the macroeconomic effects of monetary policy shocks in the U.S," "Marco Fanno" Working Papers 0181, Dipartimento di Scienze Economiche "Marco Fanno".

    More about this item

    Keywords

    Vector autoregression; heteroskedasticity; vector GARCH; conditional heteroskedasticity; Markov switching model;
    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:hum:wpaper:sfb649dp2014-009. 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: RDC-Team (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.