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Panel Data Nowcasting: The Case of Price-Earnings Ratios

Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas

arXiv 5 Jul 2023 · Econometrics · publishedJournal of Applied Econometrics (2023) · 9 citations (OpenAlex)

arXiv:2307.02673 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

24
references
51
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distinct cited
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main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Babii, Ball, Ghysels, and Striaukas (2022) Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application1.00073100%
2Ball and Ghysels (2018) Automated earnings forecasts: beat analysts or combine and conquer?0.87472100%
3Bybee, Kelly, Manela, and Xiu (2021) Business News and Business Cycles0.87472100%
4Carabias (2018) The real-time information content of macroeconomic news: implications for firm-level earnings expectations0.81142100%
5Diebold and Mariano (1995) Comparing predictive accuracy0.73732100%
6Bonhomme and Manresa (2015) Grouped patterns of heterogeneity in panel data0.64422100%
7Brown, Ghysels, and Gredil (2023) Nowcasting net asset values: The case of private equity0.64422100%
8Babii, Ghysels, and Striaukas (2022) Machine learning time series regressions with an application to nowcasting self0.51121100%
9Ferreira and Santa-Clara (2011) Forecasting stock market returns: The sum of the parts is more than the whole0.51121100%
10Foroni, Marcellino, and Schumacher (2015) Unrestricted mixed data sampling (U-MIDAS): MIDAS regressions with unrestricted lag polynomials0.40511100%

Showing the top 10 of 24 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Econometrics of Machine Learning Methods in Economic Forecasting0.51121