innovational outliers, within a forecasting framework for macroeconomic variables. Drawing on data from the COVID-19 pandemic, the … outperform both those with innovational outlier corrections and no outlier corrections in forecasting post-pandemic household …-lived extreme observations, as in the case of pandemics. These results carry important implications for macroeconomic forecasting …

forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … forecasting model that could guarantee a sound policy decisions. …

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such … as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we … incorporate Google search data into a Bridge Equation Model, a version of which usually belongs to the suite of forecasting models …

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability …. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or … variable selection and forecasting stages. In this study, we investigate whether or not we should use weighted observations at …

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related … overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by …

DSGE models have recently received considerable attention in macroeconomic analysis and forecasting. They are usually …

In this paper, we compare two fundamentally different judgmental demand forecasting approaches used to estimate demand …