What do VARs tell us about the impact of a credit supply shock?

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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.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 739

Classification and Topics

• Classification: Wirtschaft ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Business Fluctuations; Cycles
• Subject: Credit supply shocks ; Proxy SVAR ; Sign restrictions ; Identification via heteroscedasticity ; DSGE models

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mumtaz, Haroon ; Pinter, Gabor ; Theodoridis, Konstantinos
• Event: Veröffentlichung
• (who): Queen Mary University of London, School of Economics and Finance
• (where): London
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/122088