We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles...

We employ a structural VAR model with global and US variables to study the relevance and transmission of oil, food commodities, and industrial input price shocks. We show that commodities are not all alike. Industrial input price changes are almost entirely endogenous responses to other shocks....

We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles...

We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles...

We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles...

We employ a structural VAR model with global and US variables to study the relevance and transmission of oil, food commodities, and industrial input price shocks. We show that commodities are not all alike. Industrial input price changes are almost entirely endogenous responses to other shocks....