A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in...

We show that macroeconomic uncertainty can be considered as exogenous when assessing its effects on the U.S. economy. Instead, financial uncertainty can at least in part arise as an endogenous response to some macroeconomic developments, and overlooking this channel leads to distortions in the...

We derive a Bayesian prior from a no-arbitrage affine term structure model and use it to estimate the coefficients of a vector autoregression of a panel of government bond yields, specifying a common time-varying volatility for the disturbances. Results based on US data show that this method...

This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and...

We propose a new framework for measuring uncertainty and its effects on the economy, based on a large VAR model with errors whose stochastic volatility is driven by two common unobservable factors, representing aggregate macroeconomic and financial uncertainty. The uncertainty measures can also...

A knowledge of the level of trend inflation is key to many current policy decisions, and several methods of estimating trend inflation exist. This paper adds to the growing literature which uses survey-based long-run forecasts of inflation to estimate trend inflation. We develop a bivariate...

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper...

Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier...

This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, as well as classical and Bayesian quantile regressions) and also different...

This paper uses a large vector autoregression to measure international macroeconomic uncertainty and its effects on major economies. We provide evidence of significant commonality in macroeconomic volatility, with one common factor driving strong comovement across economies and variables. We...