Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Oil prices contain information on global shocks of key relevance for monetary policy decisions. We propose a novel approach to identify these shocks at the daily frequency in a Structural Vector Autoregression (SVAR). Our method is devised to be used in real time to interpret the developments in the oil market and their implications for the macroeconomy, circumventing the problem of publication lags that plagues monthly data used in workhorse SVAR models. It proves particularly valuable for monetary policymakers at times when macroeconomic conditions evolve rapidly, like during the COVID-19 pandemic or the invasion of Ukraine by Russia.