This paper documents large revisions in a widely used series of utilization-adjusted total factor productivity (TFP) by Fernald (2014) and shows that these revisions can materially affect empirical results about the effects of news shocks. We trace these revisions to changes in estimated factor utilization that are evocative of cyclical measurement issues with productivity. We propose an alternative identification that is robust to these measurement issues. Applied to U.S. data, the shock predicts delayed productivity growth while simultaneously generating strong responses of novel indicators of technological innovation and forward-looking variables. The shock does not lead to comovement in macroeconomic aggregates.

This reply to Cascaldi-Garcia's (2017) comment argues that by using the original code of Kurmann and Otrok (2013) with new data on utilization-adjusted TFP, Cascaldi-Garcia (2017) confounds positive and negative news shocks. With a small modification to the code—how a news shock is signed as positive—we obtain news shock responses consistent with Sims (2016) and Kurmann and Sims (2017) and largely reestablish the results of Kurmann and Otrok (2013).

We adopt a statistical approach to identify the shocks that explain most of the fluctuations of the slope of the term structure of interest rates. We find that one shock can explain the majority of unpredictable movements in the slope. Impulse response functions lead us to interpret this shock as news about future total factor productivity (TFP). By showing that “slope shocks” are essentially “TFP news shocks” we provide a new explanation for the relationship between the slope and macroeconomic fundamentals. Our results also provide a new empirical benchmark for structural models at the intersection of macroeconomics and finance.

Beaudry and Portier (2006) propose an identification scheme to study the effects of news shocks about future productivity in vector error correction models (VECMs). This comment shows that, when applied to their VECMs with more than two variables, the identification scheme does not have a unique solution. The problem arises from a particular interplay of cointegration assumptions and long-run restrictions.