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

Author

Listed:
  • Haroon Mumtaz

    (Queen Mary University of London)

  • Gabor Pinter

    (Bank of England)

  • Konstantinos Theodoridis

    (Bank of England)

Abstract

This paper evaluates the performance of a variety of structural VAR models in estimating the impact of credit supply shocks. Using a Monte-Carlo experiment, we show that identification based on sign and quantity restrictions and via external instruments is effective in recovering the underlying shock. In contrast, identification based on recursive schemes and heteroscedasticity suffer from a number of biases. When applied to US data, the estimates from the best performing VAR models indicate, on average, that credit supply shocks that raise spreads by 10 basis points reduce GDP growth and inflation by 1% after one year. These shocks were important during the Great Recession, accounting for about half the decline in GDP growth.

Suggested Citation

  • Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2015. "What do VARs Tell Us about the Impact of a Credit Supply Shock?," Working Papers 739, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:739
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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. repec:psc:journl:v:9:y:2017:i:4:p:323-357 is not listed on IDEAS
    4. 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.
    5. Lunsford, Kurt Graden, 2015. "Identifying Structural VARs with a Proxy Variable and a Test for a Weak Proxy," Working Papers (Old Series) 1528, Federal Reserve Bank of Cleveland.
    6. Daniel Kaufmann, 2017. "Is Deflation Costly After All? The Perils of Erroneous Historical Classifications," IRENE Working Papers 17-09, IRENE Institute of Economic Research.
    7. 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.
    8. Garreth Rule, 2015. "Understanding the central bank balance sheet," Handbooks, Centre for Central Banking Studies, Bank of England, edition 1, number 32.
    9. Bańbura, Marta & Albani, Maria & Ambrocio, Gene & Bursian, Dirk & Buss, Ginters & de Winter, Jasper & Gavura, Miroslav & Giordano, Claire & Júlio, Paulo & Le Roux, Julien & Lozej, Matija & Malthe-Thag, 2018. "Business investment in EU countries," Occasional Paper Series 215, European Central Bank.

    More about this item

    Keywords

    Credit supply shocks; Proxy SVAR; Sign restrictions; Identification via heteroscedasticity; 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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