We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

Increasingly, professional forecasters and academic researchers present model-based and subjective or judgment-based forecasts in economics which are accompanied by some measure of uncertainty. In its most complete form this measure is a probability density function for future values of the...

The Fed's policy rule switches during the different phases of the business cycle. This finding is established using a dynamic mixture model to estimate regime-dependent Taylor-type rules on US quarterly data from 1960 to 2021. Instead of exogenously partitioning the data based on tenures of the...

The Fed's policy rule shifts during different phases of the business cycle, particularly in relation to monetary easing and tightening phases. This finding is established through a dynamic mixture model, which estimates regime-dependent Taylor-type rules using US quarterly data from 1960 to...

The Fed's policy rule switches during the different phases of the business cycle. This finding is established using a dynamic mixture model to estimate regime-dependent Taylor-type rules on US quarterly data from 1960 to 2021. Instead of exogenously partitioning the data based on tenures of the...

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing...

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing...

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the...

Several Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time series....