IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/13853.html
   My bibliography  Save this paper

Identification with External Instruments in Structural VARs under Partial Invertibility

Author

Listed:
  • Miranda-Agrippino, Silvia
  • Ricco, Giovanni

Abstract

This paper discusses the conditions for identification in SVAR-IVs when only the shock of interest or a subset of the structural shocks can be recovered as a linear combination of the VAR residuals. This condition of partial invertibility is very general, often of empirical relevance, and less stringent than the standard full invertibility that is routinely assumed in the SVAR literature. We show that, under partial invertibility, the dynamic responses can be correctly recovered using an external instrument even when this correlates with leads and lags of other invertible shocks. We call this a limited lead-lag exogeneity condition. We evaluate our results in a simulated environment, and provide an empirical application to the case of monetary policy shocks.

Suggested Citation

  • Miranda-Agrippino, Silvia & Ricco, Giovanni, 2019. "Identification with External Instruments in Structural VARs under Partial Invertibility," CEPR Discussion Papers 13853, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:13853
    as

    Download full text from publisher

    File URL: http://www.cepr.org/active/publications/discussion_papers/dp.php?dpno=13853
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rabah Arezki & Valerie A. Ramey & Liugang Sheng, 2017. "News Shocks in Open Economies: Evidence from Giant Oil Discoveries," The Quarterly Journal of Economics, Oxford University Press, vol. 132(1), pages 103-155.
    2. Altavilla, Carlo & Brugnolini, Luca & Gürkaynak, Refet S. & Motto, Roberto & Ragusa, Giuseppe, 2019. "Measuring euro area monetary policy," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 162-179.
    3. Domenico Giannone & Lucrezia Reichlin, 2006. "Does information help recovering structural shocks from past observations?," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 455-465, 04-05.
    4. Ryan Chahrour & Kyle Jurado, 2017. "Recoverability," Boston College Working Papers in Economics 935, Boston College Department of Economics.
    5. Ricco, Giovanni & Callegari, Giovanni & Cimadomo, Jacopo, 2016. "Signals from the government: Policy disagreement and the transmission of fiscal shocks," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 107-118.
    6. Silvia Miranda-Agrippino & Giovanni Ricco, 2015. "The Transmission of Monetary Policy Shocks," Discussion Papers 1711, Centre for Macroeconomics (CFM), revised Feb 2017.
    7. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
    8. Hansen, Lars Peter & Sargent, Thomas J., 1980. "Formulating and estimating dynamic linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 7-46, May.
    9. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106, National Bureau of Economic Research, Inc.
    10. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
    11. Miranda-Agrippino, Silvia & Hacıoglu Hoke, Sinem, 2018. "When creativity strikes: news shocks and business cycle fluctuations," LSE Research Online Documents on Economics 90381, London School of Economics and Political Science, LSE Library.
    12. Fabio Canova & Filippo Ferroni, 2018. "Mind the gap! Stylized dynamic facts and structural models," Working Papers No 13/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    13. Dario Caldara & Edward Herbst, 2019. "Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 157-192, January.
    14. Pascal Paul, 2020. "The Time-Varying Effect of Monetary Policy on Asset Prices," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 690-704, October.
    15. Braun, Phillip A. & Mittnik, Stefan, 1993. "Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions," Journal of Econometrics, Elsevier, vol. 59(3), pages 319-341, October.
    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. Leonardo N. Ferreira, 2020. "Forward Guidance Matters: disentangling monetary policy shocks," Working Papers Series 530, Central Bank of Brazil, Research Department.
    2. Ettmeier, Stephanie & Kriwoluzky, Alexander, 2019. "Same, but different? Testing monetary policy shock measures," Economics Letters, Elsevier, vol. 184(C).
    3. Adam Brzezinski & Yao Chen & Nuno Palma & Felix Ward, 2019. "The real effects of money supply shocks: Evidence from maritime disasters in the Spanish Empire," The School of Economics Discussion Paper Series 1906, Economics, The University of Manchester.
    4. Fabio Canova & Filippo Ferroni, 2018. "Mind the gap! Stylized dynamic facts and structural models," Working Papers No 13/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. Mirela Miescu & Haroon Mumtaz, 2019. "Proxy structural vector autoregressions, informational sufficiency and the role of monetary policy," Working Papers 280730188, Lancaster University Management School, Economics Department.
    6. Mirela S. Miescu & Haroon Mumtaz, 2019. "Proxy structural vector autoregressions, informational sufficiency and the role of monetary policy," Working Papers 894, Queen Mary University of London, School of Economics and Finance.
    7. Silvia Miranda-Agrippino & Sinem Hacioglu Hoke & Kristina Bluwstein, 2018. "When Creativity Strikes: News Shocks and Business Cycle Fluctuations," Discussion Papers 1823, Centre for Macroeconomics (CFM).
    8. Hacioglu Hoke, Sinem, 2019. "Macroeconomic effects of political risk shocks," Bank of England working papers 841, Bank of England.
    9. Martínez-Hernández, Catalina, 2020. "Disentangling the effects of multidimensional monetary policy on inflation and inflation expectations in the euro area," Discussion Papers 2020/18, Free University Berlin, School of Business & Economics.
    10. Andrea Giovanni Gazzani & Alejandro Vicondoa, 2019. "Proxy-SVAR as a Bridge for Identification with Higher Frequency Data," 2019 Meeting Papers 855, Society for Economic Dynamics.

    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. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2019. "Identification with External Instruments in Structural VARs under Partial Invertibility," The Warwick Economics Research Paper Series (TWERPS) 1213, University of Warwick, Department of Economics.
    2. Cascaldi-Garcia, Danilo & Vukotic, Marija, 2019. "Patent-Based News Shocks," The Warwick Economics Research Paper Series (TWERPS) 1225, University of Warwick, Department of Economics.
    3. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2012. "Oil prices, exchange rates and emerging stock markets," Energy Economics, Elsevier, vol. 34(1), pages 227-240.
    4. Fabio Canova & Mehdi Hamidi Sahneh, 2018. "Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Nonfundamentalness," Journal of the European Economic Association, European Economic Association, vol. 16(4), pages 1069-1093.
    5. Ellahie, Atif & Ricco, Giovanni, 2017. "Government purchases reloaded: Informational insufficiency and heterogeneity in fiscal VARs," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 13-27.
    6. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "A Review of Nonfundamentalness and Identification in Structural VAR Models," LEM Papers Series 2007/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Alfred A. Haug & Christie Smith, 2012. "Local Linear Impulse Responses for a Small Open Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(3), pages 470-492, June.
    8. Leonardo N. Ferreira, 2020. "Forward Guidance Matters: disentangling monetary policy shocks," Working Papers Series 530, Central Bank of Brazil, Research Department.
    9. Miranda-Agrippino, Silvia & Hacıoglu Hoke, Sinem, 2018. "When creativity strikes: news shocks and business cycle fluctuations," LSE Research Online Documents on Economics 90381, London School of Economics and Political Science, LSE Library.
    10. Mirela Miescu & Haroon Mumtaz, 2019. "Proxy structural vector autoregressions, informational sufficiency and the role of monetary policy," Working Papers 280730188, Lancaster University Management School, Economics Department.
    11. Kortela, Tomi & Nelimarkka, Jaakko, 2020. "The effects of conventional and unconventional monetary policy : identification through the yield curve," Research Discussion Papers 3/2020, Bank of Finland.
    12. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    13. Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
    14. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    15. Hansen, James & Gross, Isaac, 2018. "Commodity price volatility with endogenous natural resources," European Economic Review, Elsevier, vol. 101(C), pages 157-180.
    16. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    18. Yuliya Lovcha & Alejandro Perez-Laborda, 2017. "Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market," Empirical Economics, Springer, vol. 53(2), pages 405-422, September.
    19. Kilian, Lutz & Lee, Thomas K., 2014. "Quantifying the speculative component in the real price of oil: The role of global oil inventories," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 71-87.
    20. Nelimarkka, Jaakko, 2017. "Evidence on News Shocks under Information Deficiency," MPRA Paper 80850, University Library of Munich, Germany.

    More about this item

    Keywords

    Identification with External Instruments; Invertibility; monetary policy shocks; structural VAR;
    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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cpr:ceprdp:13853. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: https://www.cepr.org .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.