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DSGE Model Restrictions for Structural VAR Identification

Author

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  • 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
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    Cited by:

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    2. Jakub Matějů, 2019. "What Drives the Strength of Monetary Policy Transmission?," International Journal of Central Banking, International Journal of Central Banking, vol. 15(3), pages 59-87, September.
    3. Lukmanova, Elizaveta & Rabitsch, Katrin, 2018. "New VAR evidence on monetary transmission channels: temporary interest rate versus inflation target shocks," Department of Economics Working Paper Series 274, WU Vienna University of Economics and Business.
    4. Jakub Mateju, 2013. "Explaining the Strength and the Efficiency of Monetary Policy Transmission: A Panel of Impulse Responses from a Time-Varying Parameter Model," Working Papers IES 2013/18, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2013.
    5. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    6. 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(1), pages 167-188, January.
    7. 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.
    8. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    9. Renata Wróbel-Rotter, 2016. "Impulse Response Functions in the Dynamic Stochastic General Equilibrium Vector Autoregression Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(2), pages 93-114, June.
    10. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2018. "What Do Vars Tell Us About The Impact Of A Credit Supply Shock?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 625-646, May.
    11. 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.
    12. Gehrke, Britta & Yao, Fang, 2013. "Sources of Real Exchange Rate Fluctuations: The Role of Supply Shocks Revisited," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79821, Verein für Socialpolitik / German Economic Association.
    13. Adrian Pagan & Tim Robinson, 2016. "Investigating the Relationship Between DSGE and SVAR Models," NCER Working Paper Series 112, National Centre for Econometric Research.
    14. Tim Robinson, 2013. "Estimating and Identifying Empirical BVAR-DSGE Models for Small Open Economies," RBA Research Discussion Papers rdp2013-06, Reserve Bank of Australia.
    15. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2018. "What Do Vars Tell Us About The Impact Of A Credit Supply Shock?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 625-646, May.
    16. K. Istrefi & B. Vonnak, 2015. "Delayed Overshooting Puzzle in Structural Vector Autoregression Models," Working papers 576, Banque de France.

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    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

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