Abstract

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.

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• Keyword(s):
• economics 46
• finance 247
• machine learning 22

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