Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In a recent paper, Berger et al. (2023) employ the Beveridge–Nelson trend-cycle decomposition based on a mixed-frequency Bayesian vector autoregressive model to nowcast the U.S. output gap, producing more timely estimates compared to a set of alternative measures. Applying the model to a much shorter and slightly modified data set, we show that utilizing shadow interest rates instead of the federal funds rate in the model produces output gap estimates that are more in line with other measures such as those provided by the CBO or the IMF, and further enhances the timeliness of nowcasts.