Uncertainty and Monetary Policy during Extreme Events - Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano (Center for Economic Studies and Ifo Institute (CESifo), 2020)
How damaging are uncertainty shocks during extreme events such as the great recession and the Covid-19 outbreak? Can monetary policy limit output losses in such situations? We use a nonlinear VAR framework to document the large response of real activity to a financial uncertainty shock during the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept of uncertainty comparable to that in our VAR. We employ the DSGE model to quantify the impact on real activity of an uncertainty shock under different Taylor rules estimated with normal times vs. great recession data (the latter associated with a stronger response to output). We find that the uncertainty shock-induced output loss experienced during the 2007-09 recession could have been twice as large if policymakers had not responded aggressively to the abrupt drop in output in 2008Q3. Finally, we use our estimated DSGE framework to simulate different paths of uncertainty associated to different hypothesis on the evolution of the coronavirus pandemic. We find that: i) Covid-19-induced uncertainty could lead to an output loss twice as large as that of the great recession; ii) aggressive monetary policy moves could reduce such loss by about 50%.
Keywords: G22; Q54; R11; R31; 330; house price prediction; machine learning; genetic algorithm; spatial aggregation