The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection...

In many macroeconomic forecasting applications factor models are used to cope with large datasets. This study aligns variational autoencoders with macroeconomic factor modeling and proposes an extension to adapt this framework for forecasting exercises. Variational autoencoders are well suited...

The primary objective of this paper is to propose two nonlinear extensions for macroeconomic forecasting using large datasets. First, we propose an alternative technique for factor estimation, i.e., kernel principal component analysis, which allows the factors to have a nonlinear relationship to...

We develop metrics based on Shapley values for interpreting time-series forecasting models, including “black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an...

Our purpose is to verify the predictive performances of the artificial neural networks (ANNs) under volatile statistics and possibly incomplete information. Daily forecasts of exchange rate using exclusively primary available information for an emergent economy (such as the Romanian one) could...