In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed (U.S.) and emerging-market economy (Brazil). Our focus is on the marginal contribution of "Big Data" in the form of Google Trends data over and above that of traditional...

This paper studies the comparative predictive accuracy of forecasting methods using mixed-frequency data, as applied to nowcasting Philippine inflation, real GDP growth, and other related macroeconomic variables. It focuses on variations of mixed-frequency dynamic latent factor models (DFM for...

We propose a Release-Augmented Dynamic Factor Model (RA-DFM) that allows to quantify the role of a country's data flow in nowcasting both early GDP releases, and subsequent revisions of official estimates. We use the RA-DFM to study UK GDP early revision rounds, and assemble a comprehensive and...

By employing large panels of survey data for the UK economy, we aim at reviewing linear approaches for regularisation and dimension reduction combined with techniques from the machine learning literature, like Random Forests, Support Vector Regressions and Neural Networks for forecasting GDP...

The Covid-19 crisis has shown how high-frequency data can help tracking economic turning points in real-time. Our paper investigates whether high-frequency data can also improve the nowcasting performances for world GDP growth on quarterly or annual basis. To this end, we select a large dataset...

Common sense tells that historical data are more informative for the estimation of today's nowcasting models when observed in a similar economic state as today. We operationalise this intuition by proposing a state-based weighted estimation procedure of GDP nowcasting models, in which...

We evaluate conditional predictive densities for U.S. output growth and inflation using a number of commonly used forecasting models that rely on a large number of macroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used...

We analyze the performance of a broad range of nowcasting and short-term forecasting models for a representative set of twelve old and six new member countries of the European Union (EU) that are characterized by substantial differences in aggregate output variability. In our analysis, we...

A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum Schwarz Bayesian Information Criterion (Minimum...

We apply the two-step machine-learning method proposed by Claveria et al. (2021) to generate country-specific sentiment indicators that provide estimates of year-on-year GDP growth rates. In the first step, by means of genetic programming, business and consumer expectations are evolved to derive...