Nowcasting the output gap with shadow rates
在Berger等人(2023)的混频贝叶斯向量自回归模型基础上，用影子利率替代联邦基金利率来实时预测美国产出缺口，得到的估计更符合CBO或IMF等机构的数据，且预测更及时。
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.