The common practice for GDP nowcasting in a data-rich environment is to employ either sparse regression using LASSO-type regularization or a dense approach based on factor models or ridge regression, which differ in the way they extract information from high-dimensional datasets. This paper aims to investigate whether sparse plus dense mixed frequency regression methods can improve the nowcasts of the US GDP growth. We propose two novel MIDAS regressions and show that these novel sparse plus dense methods greatly improve the accuracy of nowcasts during the COVID pandemic compared to either only sparse or only dense approaches. Using monthly macro and weekly financial series, we further show that the improvement is particularly sharp when the dense component is restricted to be macro, while the sparse signal stems from both macro and financial series.

The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data. Using a panel of 19 Euro area countries, we investigate whether directly nowcasting the Euro area aggregate is better than weighted individual country nowcasts. Our results highlight the importance of the information from small- and medium-sized countries, particularly when including the COVID-19 pandemic period. The empirical analysis is supplemented by studying the so-called Big Four -- France, Germany, Italy, and Spain -- and the value added of news data when official statistics are lagging. From a theoretical perspective, we formally show that the aggregation of individual components forecasted with pooled panel data regressions is superior to direct aggregate forecasting due to lower estimation error.