  The paper uses structured machine learning regressions for nowcasting with
panel data consisting of series sampled at different frequencies. Motivated by
the problem of predicting corporate earnings for a large cross-section of firms
with macroeconomic, financial, and news time series sampled at different
frequencies, we focus on the sparse-group LASSO regularization which can take
advantage of the mixed frequency time series panel data structures. Our
empirical results show the superior performance of our machine learning panel
data regression models over analysts' predictions, forecast combinations,
firm-specific time series regression models, and standard machine learning
methods.
