Purpose This study aims to evaluate linear mixed data sampling (MIDAS), nonlinear artificial
neural networks (ANNs) and a hybrid approach for exploiting high-frequency information to …

Abstracts This study examines quarterly data from China spanning 1993 to 2016, employing
machine learning algorithms to investigate the predictive ability of money and credit on …

This study addresses the challenge of nowcasting Gross Domestic Product (GDP) in data-
scarce environments, with a focus on Syria, a country facing significant economic and …

We reinvigorate nowcasting models considering structural changes caused by the COVID-
19 pandemic. It emphasizes the need to understand the heterogeneous impact of shocks on …

We investigate the predictive power of different machine learning algorithms to nowcast
Madagascar's gross domestic product (GDP). We trained popular regression models …

In the dynamic landscape of continuous change, Machine Learning (ML)" nowcasting"
models offer a distinct advantage for informed decision-making in both public and private …

In a context of ongoing change,“nowcasting” models based on Machine Learning (ML)
algorithms deliver a noteworthy advantage for decision-making in both the public and …

The GDP forms an essential metric in assessing the state of the economy. However, the
GDP figures are only available with a certain lag whereas economists need the data on a …

In a context of ongoing change,“nowcasting” models based on Machine Learning (ML)
algorithms deliver a noteworthy advantage for decision-making in both the public and …

This study presents a comparative analysis of machine learning models, specifically
gradient boosting machine (GBM) and random forest (RF), against the traditional vector …