Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Focusing on U.S. GDP,...

Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Focusing on U.S. GDP,...

The crisis periods of the past decades have highlighted the difficulty of forecasting economic indicators due to increased non-linearity and rapidly changing dynamics. To address this challenge, we introduce the Transform-Sparsify-Forecast (TSF) framework. The TSF framework first applies...

Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against...

Lawrence R. Klein (September 14, 1920 – October 20, 2013), Nobel Laureate in Economic Sciences in 1980, was one of the leading figures in macro-econometric modeling. Although his contributions to forecasting using simultaneous equations macro models were very well known, his contributions to...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

Forecasting economic activity during an invasion is a nontrivial exercise. The lack of timely statistical data and the expected nonlinear effect of military action challenge the use of established nowcasting and shortterm forecasting methodologies. In a recent study (Constantinescu (2023b)), I...

An essential element of the work of the Fund is to monitor and forecast international trade. This paper uses SWIFT messages on letters of credit, together with crude oil prices and new export orders of manufacturing Purchasing Managers' Index (PMI), to improve the short-term forecast of...

In this paper, we compare two popular statistical learning techniques, logistic regression and random forest, with respect to their ability to classify jobseekers by their likelihood to become long-term unemployed. We study the performance of the two methods before the COVID-19 pandemic as well...

This paper introduces the OECD Weekly Tracker of economic activity for 46 OECD and G20 countries using Google Trends search data. The Tracker performs well in pseudo-real time simulations including around the COVID-19 crisis. The underlying model adds to the previous Google Trends literature in...