This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence … Purchasing Managers Indices (PMIs) for forecasting global (48 countries) growth, and compare forecasts from AugGVAR models with a … number of data-rich forecasting methods, including Lasso, Ridge, partial least squares and factor-based methods. It is found …

-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature … for forecasting consumption developments. …

paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set …-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application …

information. We explore several new forecasting approaches for the U.S. retail price of gasoline and compare their accuracy with … successful forecasting models. Pooled forecasts have lower MSPE than the EIA gasoline price forecasts and the gasoline price …

nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that …

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one … forecasting a wide range of macroeconomic variables. Moreover, we analyse to what extent its forecasting accuracy depends on the … commonly used information criteria. -- macroeconomic forecasting ; component-wise boosting ; large datasets ; variable …

This paper applies component-wise boosting to the topic of regional economic forecasting. Component-wise boosting is a … pre-selection algorithm of indicators for forecasting. By using unique quarterly real gross domestic product data for two … economic forecasting. We additionally take a closer look into the algorithm and ask which indicators get selected. All in all …