Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In the aftermath of the recent financial crisis, a variety of structural vector autoregression (VAR) models have been proposed to identify credit supply shocks. Using a Monte Carlo experiment, we show that the performance of these models can vary substantially, with some identification schemes producing particularly misleading results. When applied to U.S. 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.