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

Author

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  • Haroon Mumtaz

    () (Queen Mary University of London)

  • Gabor Pinter

    (Bank of England)

  • Konstantinos Theodoridis

    (Bank of England)

Abstract

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.

Suggested Citation

  • 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.
  • Handle: RePEc:qmw:qmwecw:wp716
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Kok, Christoffer & Gross, Marco & Żochowski, Dawid, 2016. "The impact of bank capital on economic activity - evidence from a mixed-cross-section GVAR model," Working Paper Series 1888, European Central Bank.
    2. Kanngiesser, Derrick & Martin, Reiner & Maurin, Laurent & Moccero, Diego, 2017. "Estimating the impact of shocks to bank capital in the euro area," Working Paper Series 2077, European Central Bank.
    3. Altavilla, Carlo & Darracq Pariès, Matthieu & Nicoletti, Giulio, 2015. "Loan supply, credit markets and the euro area financial crisis," Working Paper Series 1861, European Central Bank.
    4. Lunsford, Kurt Graden, 2015. "Identifying Structural VARs with a Proxy Variable and a Test for a Weak Proxy," Working Paper 1528, Federal Reserve Bank of Cleveland.
    5. Daniel Kaufmann, 2017. "Is Deflation Costly After All? The Perils of Erroneous Historical Classifications," IRENE Working Papers 17-09, IRENE Institute of Economic Research.
    6. Jentsch, Carsten & Lunsford, Kurt G., 2016. "Proxy SVARs : asymptotic theory, bootstrap inference, and the effects of income tax changes in the United States," Working Papers 16-10, University of Mannheim, Department of Economics.
    7. Garreth Rule, 2015. "Understanding the central bank balance sheet," Handbooks, Centre for Central Banking Studies, Bank of England, edition 1, number 32.
    8. repec:psc:journl:v:9:y:2017:i:4:p:323-357 is not listed on IDEAS

    More about this item

    Keywords

    Credit supply shocks; Proxy SVAR; Sign restrictions; DSGE models;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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