Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

Abstract

This paper introduces structured machine learning regressions for nowcasting and forecasting with panel data consisting of series sampled at different frequencies. Motivated by the empirical 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. This type of regularization can take advantage of the mixed frequency time series panel data structures and we find that it empirically outperforms the unstructured machine learning methods. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data can have fat tails. To that end, we leverage on a new Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed $\tau$-mixing processes. Our empirical results show the superior performance of introduced machine learning panel data regressions over analysts' predictions, forecast combinations, firm-specific time series regression models, and unstructured elastic net.