Panel Data Nowcasting: The Case of Price-Earnings Ratios

2023/07/05 by Andrii Babii, Babii, Andrii, Ryan T. Ball +5 · 2 citations
Decision Sciences · Economics, Econometrics and Finance · #Applications (stat.AP) #Computation (stat.CO) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (stat.ML) #Monetary Policy and Economic Impact #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2307.02673

openalex publication_date 2023/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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