This article introduces structured machine learning regressions for high-dimensional time
series data potentially sampled at different frequencies. The sparse-group LASSO estimator …

This paper performs the nowcasting of GDP growth rate and inflation expectation in China
with traditional macroeconomic and novel textual data estimated by the latent Dirichlet …

Technological advancements in the field of electrical energy distribution and utilization are
revolutionizing the way consumers and utility providers interact. In addition to allowing utility …

In today's interconnected world of widespread mobility, ubiquitous social interaction, and
rapid information dissemination, the demand for individuals to swiftly adapt their behaviors …

Detailed labour market and economic data are often released infrequently and with
considerable time lags between collection and release, making it difficult for policy‐makers …

Large and acute economic shocks such as the 2007-2009 financial crisis and the current
COVID-19 infections rapidly change the economic environment. In such a situation, real-time …

The ability of machine learning models to forecast unemployment rates better than
traditional statistical methods has been well established in literature. The ambition of …

As economic uncertainties have increased recently due to COVID-19, there is a growing
need to quickly grasp private consumption trends that directly reflect the economic situation …

Job posting counts (JPCs) are emerging as indicators of employment dynamics, yet their
validity requires assessment. This study evaluates the effectiveness of big-data–based JPCs …

Predictors of macroeconomic indicators rely primarily on traditional data sourced from
National Statistical Offices. However, new data sources made available from recent …