News Shocks under Financial Frictions: A Comment on Görtz et al. (2022)
G¨ortz et al. (2022) estimate the effects of innovations to future total factor productivity (TFP) on financial markets. In a Bayesian vector autoregression, they identify a TFP news shock as one that explains the largest share of 40-quarter ahead forecast error variance (FEV) of TFP. Their estimated impulse responses functions show that a positive news shock significantly decreases credit market spreads and increases credit market supply. They also find that a shock that explains the maximum of the FEV of the “excess bond premium” (EBP) (Gilchrist and Zakrajsek 2012) causes similar responses. These results are consistent with an estimated DSGE model with financial frictions.
We estimate the main IRFs of the study using the original data and a frequentist estimation approach. We obtain similar point estimates for the dynamic responses to TFP news and EBP max-share shocks. We also update their macroeconomic and financial time series, as some of the data has been revised substantially since their original estimate. We use the updated data to re-estimate the above-mentioned IRFs, and we find that the results are robust to this change in the data. Finally, we investigate the computational reproducibility of their DSGE results, and find that their provided code (consistent with warnings in their README file) does not execute in the most recent version of Dynare or Matlab. Using the version indicated in their replication files, we encounter issues estimating the posterior mode.