Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/122088
Authors: 
Mumtaz, Haroon
Pinter, Gabor
Theodoridis, Konstantinos
Year of Publication: 
2015
Series/Report no.: 
Working Paper, School of Economics and Finance, Queen Mary, University of London 739
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.
Subjects: 
Credit supply shocks
Proxy SVAR
Sign restrictions
Identification via heteroscedasticity
DSGE models
JEL: 
C15
C32
E32
Document Type: 
Working Paper

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