Panel data nowcasting: The case of price–earnings ratios
用结构化机器学习回归方法对混合频率面板数据进行即时预测，以预测企业盈利为例，发现稀疏组LASSO模型优于分析师预测和传统方法。
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.