We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence the dynamics of the coefficients in these models. An estimation algorithm and a parametrization conducive to model comparison are also...

We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence - contemporaneously and with a lag - the dynamics of the intercept and autoregressive coefficients in these models. An estimation algorithm...

We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a large dataset of disaggregated industrial production series for the US economy. Our results...

The rise in inflation in 2021-22 sparked a growing literature and debate over the causes of the surge as well as the near- and medium-term path for inflation. This review offers three key messages. First, the exceptional nature of shocks resulting from the COVID-19 pandemic and geopolitical...

We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a large dataset of disaggregated industrial production series for the US economy. Our results...

We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a large dataset of disaggregated industrial production series for the US economy. Our results...