We use a machine-learning approach known as boosted regression trees (BRT) to
reexamine the usefulness of selected leading indicators for predicting recessions. We …

Interest in the use of “big data” when it comes to forecasting macroeconomic time series
such as private consumption or unemployment has increased; however, applications to the …

We estimate a Markow-switching dynamic factor model with three states based on six
leading business cycle indicators for Germany, preselected from a broader set using the …

We present a machine-learning method for sentiment indicators construction that allows an
automated variable selection procedure. By means of genetic programming, we generate …

We present a first assessment of the predictive ability of machine learning methods for
inflation forecasting in Costa Rica. We compute forecasts using two variants of k-nearest …

Abstract We estimate Boosted Regression Trees (BRT) on a sample of monthly data that
extends back to 1889 to recover the predictive value of disaggregated news-based …

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 …

We present a comprehensive disaggregate approach for short-term forecasting economic
activity in Germany by explicitly taking into account the supply or production side and the …

This paper aims to elaborate a treated data set and apply the boosting methodology to
monthly Brazilian macroeconomic variables to check its predictability. The forecasting …

This paper seeks to test the component-wise boosting validity as an instrument of
forecasting regional series in economic recessions. We use 822 predictors to forecast the …