While episodes of financial distress are followed by large and persistent drops in economic activity, structural time series analyses point to relatively mild and transitory effects of financial market disruptions. We argue that these seemingly contradictory findings are due to the asymmetric effects of financial shocks, which have been predicted theoretically but not taken into account empirically. We estimate a model designed to identify the (possibly asymmetric) effects of financial market disruptions, and we find that a favorable financial shock—an easing of financial conditions—has little effect on output, but an adverse shock has large and persistent effects. In a counterfactual exercise, we find that over two-thirds of the gap between current US GDP and its 207 precrisis trend was caused by the 2007–2008 financial shocks.

Local projections (LP) is a popular methodology for the estimation of impulse responses (IR). Compared to the traditional VAR approach, LP allow for more flexible IR estimation by imposing weaker assumptions on the dynamics of the data. The nonparametric nature of LP comes at an efficiency cost, and in practice, the LP estimator may suffer from excessive variability. In this work, we propose an IR estimation methodology based on B-spline smoothing called smooth local projections (SLP). The SLP approach preserves the flexibility of standard LP, can substantially increase precision, and is straightforward to implement. A simulation study shows that SLP can deliver substantial gains in IR estimation over LP. We illustrate our technique by studying the effects of monetary shocks where we highlight how SLP can easily incorporate commonly employed structural identification strategies.

The unemployment rate is one of the most important business cycle indicators, but its interpretation can be difficult because slow changes in the demographic composition of the labor force affect the level of unemployment and make comparisons across business cycles difficult. To purge the unemployment rate from demographic factors, labor force shares are routinely used to control for compositional changes. This paper shows that this approach is ill defined, because the labor force share of a demographic group is mechanically linked to that group's unemployment rate, as both variables are driven by the same underlying worker flows. We propose a new demographic-adjustment procedure that uses a dynamic factor model for the worker flows to separate aggregate labor market forces and demographic-specific trends. Using the U.S. labor market as an illustration, our demographic-adjusted unemployment rate indicates that the 2008–2009 recession was much more severe and generated substantially more slack than the early 1980s recession.