using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear …

series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to …

dependent and independent variables are co-integrated. In this paper, we investigate forecasting performance between first …-of-sample forecasting under the CCAR framework. A simple application for models constructed for banks’ Comprehensive Capital Analysis and …

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 …

In this paper we consider modeling and forecasting of large realized covariance matrices by penalized vector …

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 …