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Motivated by the problem of predicting corporate earnings for a large cross\u2010section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse\u2010group LASSO regularization which can take advantage of the mixed\u2010frequency 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\u2010specific time series regression models, and standard machine learning methods.<\/jats:p>","DOI":"10.1002\/jae.3028","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T06:50:58Z","timestamp":1703659858000},"page":"292-307","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Panel data nowcasting: The case of price\u2013earnings ratios"],"prefix":"10.1002","volume":"39","author":[{"given":"Andrii","family":"Babii","sequence":"first","affiliation":[{"name":"University of North Carolina at Chapel Hill\u2014Gardner Hall  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan T.","family":"Ball","sequence":"additional","affiliation":[{"name":"Stephen M. 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