We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

The Global Financial Crisis established that policymakers should consider the stage of the financial cycle to better evaluate the cyclical position of the economy when designing monetary policy decisions. If financial variables are omitted from the estimations of the output gap, a common and...

This paper presents a new framework allowing strategic investors to generate yield curve projections contingent on expectations about future macroeconomic scenarios. By consistently linking the shape and location of yield curves to the state of the economy our method generates predictions for...

We assess the ability of yield curve factors to predict risk premia in short-term interest rates and exchange rates across a large sample of major advanced economies. We find that the same tick-shaped linear combination of (relative) bond yields predicts risk premia in both short-term interest...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to...

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...