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

principle components and other shrinkage techniques, including Bayesian model averaging and various bagging, boosting, least …/2 of the time in 4 of 6 different sample periods. Ancillary findings based on our forecasting experiments underscore the …

based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper. …

regression trees, bagging, random forest, boosting machines and neural networks. Finally, we provide methodologies for analysing …

. Conversely, in countries where expectations show a smooth transition towards recession, ARIMA models show the best forecasting …

, boosting, mixtures of models, computational complexity, computational statistics, and nonlinear models in general. Although …

dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting …

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 …