IDEAS home Printed from https://ideas.repec.org/a/ijc/ijcjou/y2012q4a3.html
   My bibliography  Save this article

DSGE Model Restrictions for Structural VAR Identification

Author

Listed:
  • Philip Liu

    (International Monetary Fund)

  • Konstantinos Theodoridis

    (Bank of England)

Abstract

The identification of reduced-form VAR models has been the subject of numerous debates in the literature. Different sets of identifying assumptions can lead to very different conclusions regarding the effects of shocks. This paper proposes a theoretically consistent identification strategy using restrictions implied by a DSGE model. Monte Carlo simulations suggest that both quantitative and qualitative restrictions work well together, where they act as complements to each other, in minimizing errors in finding the correct VAR identification. When using misspecified model restrictions, the data tend to push the identified VAR responses away from the misspecified model and closer to the true data-generating process.

Suggested Citation

  • Philip Liu & Konstantinos Theodoridis, 2012. "DSGE Model Restrictions for Structural VAR Identification," International Journal of Central Banking, International Journal of Central Banking, vol. 8(4), pages 61-95, December.
  • Handle: RePEc:ijc:ijcjou:y:2012:q:4:a:3
    as

    Download full text from publisher

    File URL: http://www.ijcb.org/journal/ijcb12q4a3.pdf
    Download Restriction: no

    File URL: http://www.ijcb.org/journal/ijcb12q4a3.htm
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Thomas J. Sargent & Mark W. Watson, 2007. "ABCs (and Ds) of Understanding VARs," American Economic Review, American Economic Association, vol. 97(3), pages 1021-1026, June.
    2. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    3. Renée Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-960, December.
    4. Kapetanios, G. & Pagan, A. & Scott, A., 2007. "Making a match: Combining theory and evidence in policy-oriented macroeconomic modeling," Journal of Econometrics, Elsevier, vol. 136(2), pages 565-594, February.
    5. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711.
    6. Lütkepohl, Helmut & Poskitt, D.S., 1991. "Estimating Orthogonal Impulse Responses via Vector Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 7(04), pages 487-496, December.
    7. 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.
    8. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    9. Marco Del Negro & Frank Schorfheide & Frank Smets & Raf Wouters, 2004. "On the fit and forecasting performance of New Keynesian models," FRB Atlanta Working Paper 2004-37, Federal Reserve Bank of Atlanta.
    10. Gert Peersman & Roland Straub, 2009. "Technology Shocks And Robust Sign Restrictions In A Euro Area Svar," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 727-750, August.
    11. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, May.
    12. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    13. Paustian Matthias, 2007. "Assessing Sign Restrictions," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-33, August.
    14. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    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. Jakub Mateju, 2014. "Explaining the Strength and Efficiency of Monetary Policy Transmission: A Panel of Impulse Responses from a Time-Varying Parameter Model," Working Papers 2014/04, Czech National Bank, Research Department.
    2. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    3. Charles, Amélie & Darné, Olivier & Tripier, Fabien, 2015. "Are Unit Root Tests Useful In The Debate Over The (Non)Stationarity Of Hours Worked?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(01), pages 167-188, January.
    4. Tielens, J. & van Aarle, B. & Van Hove, J., 2014. "Effects of Eurobonds: A stochastic sovereign debt sustainability analysis for Portugal, Ireland and Greece," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 156-173.
    5. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    6. repec:wly:iecrev:v:59:y:2018:i:2:p:625-646 is not listed on IDEAS
    7. Jan Babecky & Michal Franta & Jakub Rysanek, 2016. "Effects of Fiscal Policy in the DSGE-VAR Framework: The Case of the Czech Republic," Working Papers 2016/09, Czech National Bank, Research Department.
    8. Gehrke, Britta & Yao, Fang, 2013. "Sources of Real Exchange Rate Fluctuations: The Role of Supply Shocks Revisited," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79821, Verein für Socialpolitik / German Economic Association.
    9. Adrian Pagan & Tim Robinson, 2016. "Investigating the Relationship Between DSGE and SVAR Models," NCER Working Paper Series 112, National Centre for Econometric Research.
    10. Tim Robinson, 2013. "Estimating and Identifying Empirical BVAR-DSGE Models for Small Open Economies," RBA Research Discussion Papers rdp2013-06, Reserve Bank of Australia.
    11. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2014. "What do VARs Tell Us about the Impact of a Credit Supply Shock? An Empirical Analysis," Working Papers 716, Queen Mary University of London, School of Economics and Finance.
    12. K. Istrefi & B. Vonnak, 2015. "Delayed Overshooting Puzzle in Structural Vector Autoregression Models," Working papers 576, Banque de France.

    More about this item

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    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:ijc:ijcjou:y:2012:q:4:a:3. 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: (Bank for International Settlements). General contact details of provider: https://www.ijcb.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.