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What do VARs Tell Us about the Impact of a Credit Supply Shock? An Empirical Analysis

  • Haroon Mumtaz


    (Queen Mary University of London)

  • Gabor Pinter

    (Bank of England)

  • Konstantinos Theodoridis

    (Bank of England)

This paper evaluates the performance of structural VAR models in estimating the impact of credit supply shocks. In a simple Monte-Carlo experiment, we generate data from a DSGE model that features bank lending and credit supply shocks and use SVARs to try and recover the impulse responses to these shocks. The experiment suggests that a proxy VAR that uses an instrumental variable procedure to estimate the impact of the credit shock performs well and is relatively robust to measurement error in the instrument. A structural VAR with sign restrictions also performs well under some circumstances. In contrast, VARs of the narrative variety, i.e. VAR models that include measures of the credit shock as endogenous variables are highly sensitive to ordering and measurement error. An application of the proxy VAR model and the VAR with sign restrictions to US data suggests, however, that the credit supply shock is hard to identify in practice.

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Paper provided by Queen Mary University of London, School of Economics and Finance in its series Working Papers with number 716.

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Date of creation: Apr 2014
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Handle: RePEc:qmw:qmwecw:wp716
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  1. Gertler, Mark & Gilchrist, Simon, 1994. "Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms," The Quarterly Journal of Economics, MIT Press, vol. 109(2), pages 309-40, May.
  2. William F. Bassett & Mary Beth Chosak & John C. Driscoll & Egon Zakrajsek, 2012. "Changes in bank lending standards and the macroeconomy," Finance and Economics Discussion Series 2012-24, Board of Governors of the Federal Reserve System (U.S.).
  3. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
  4. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
  5. G. Peersman, 2011. "Bank Lending Shocks and the Euro Area Business Cycle," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/766, Ghent University, Faculty of Economics and Business Administration.
  6. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  7. GONÇALVES, Silvia & KILIAN, Lutz, 2003. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Cahiers de recherche 2003-01, Universite de Montreal, Departement de sciences economiques.
  8. Markku Lanne & Helmut Luetkepohl & Katarzyna Maciejowska, 2009. "Structural Vector Autoregressions with Markov Switching," Economics Working Papers ECO2009/06, European University Institute.
  9. Renee Fry & Adrian Pagan, 2010. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," CAMA Working Papers 2010-22, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  10. Matteo Iacoviello & Marina Pavan, 2009. "Housing and debt over the life cycle and over the business cycle," Working Papers 09-12, Federal Reserve Bank of Boston.
  11. 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.
  12. Liu, Philip & Theodoridis, Konstantinos, 2010. "DSGE model restrictions for structural VAR identification," Bank of England working papers 402, Bank of England.
  13. Smets, Frank & Wouters, Raf, 2007. "Shocks and frictions in US business cycles: a Bayesian DSGE approach," Working Paper Series 0722, European Central Bank.
  14. 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.
  15. Eickmeier, Sandra & Ng, Tim, 2011. "How Do Credit Supply Shocks Propagate Internationally? A GVAR approach," CEPR Discussion Papers 8720, C.E.P.R. Discussion Papers.
  16. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
  17. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," CEPR Discussion Papers 8364, C.E.P.R. Discussion Papers.
  18. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
  19. Anil K Kashyap & Jeremy C. Stein & David W. Wilcox, 1992. "Monetary Policy and Credit Conditions: Evidence From the Composition of External Finance," NBER Working Papers 4015, National Bureau of Economic Research, Inc.
  20. Carlstrom, Charles T. & Fuerst, Timothy S. & Paustian, Matthias, 2009. "Monetary policy shocks, Choleski identification, and DNK models," Journal of Monetary Economics, Elsevier, vol. 56(7), pages 1014-1021, October.
  21. Juan F. Rubio-Ramírez & Daniel F.Waggoner & Tao Zha, 2008. "Structural vector autoregressions: theory of identification and algorithms for inference," FRB Atlanta Working Paper No. 2008-18, Federal Reserve Bank of Atlanta.
  22. Gambetti, Luca & Musso, Alberto, 2012. "Loan supply shocks and the business cycle," Working Paper Series 1469, European Central Bank.
  23. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
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