Panel data nowcasting

The case of price–earnings ratios

Bibliographic Data

ID21653506
AuthorsAndrii Babii (0000-0002-3996-6051, University of North Carolina at Chapel Hill—Gardner Hall Chapel Hill North Carolina USA), Ryan Ball (0000-0002-3707-6725, University of Michigan), Ryan T Ball (Stephen M. Ross School of Business University of Michigan Ann Arbor Michigan USA), Éric Ghysels (0000-0002-7125-2040, Department of Economics and Kenan‐Flagler Business School University of North Carolina—Chapel Hill Chapel Hill North Carolina USA, corresponding author), Jonas Striaukas (0000-0001-7658-8638, Department of Finance Copenhagen Business School Frederiksberg Denmark)
Year2024
Volume39
Issue2
Pages292-307
Publication date2024-03-01
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.3028
OpenAlexW4390272205
LanguageEN
Citations received4
References cited21

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

Earnings · Econometrics · Economics · Machine learning · Nowcasting · Panel data · Regression · Statistics · Time series · Computer Science · Financial Markets and Investment Strategies · Financial Risk and Volatility Modeling · Mathematics · Monetary Policy and Economic Impact · Finance

Tools

Open DOIOpen Access