This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying....

This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a...

We develop a new Bayesian estimator that is able to deal with multivariate panel data structure in the presence of spatial correlation. The analysis of panel data introduced here allows us to analyze not only the fixed effect but also the random effect model. This work extends the previous study...

Using federal funds futures data, we show the importance of surprise communication as a component of monetary policy for U.S. macro variables, both before and after 2008. While Gürkaynak et al. (2005) stress the importance of monetary policy communication for asset prices, much of the...

Most analyses of the U.S. Great Moderation have been based on VAR methods, and have consistently pointed toward good luck as the main explanation for the greater macroeconomic stability of recent years. Using data generated by a New-Keynesian model in which the only source of change is the move...

Structural VAR studies disagree with narrative accounts about the history of monetary policy disturbances. We investigate whether employing the narrative monetary shock account as a proxy variable in a VAR model aligns both shock series. We quantify the extent to which the disagreement still...