# Panel Data Nowcasting: The Case of Price-Earnings Ratios

arXiv: https://arxiv.org/abs/2307.02673 · HTML: https://mlsanity.com/paper/2307.02673/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [High-Dimensional Granger Causality Tests with an Application to VIX and News](https://mlsanity.com/paper/1912.06307/) (2019)
- [Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application](https://mlsanity.com/paper/2008.03600/) (2020)

## Built on by (most cited first, 2 of 2)

- [Econometrics of Machine Learning Methods in Economic Forecasting](https://mlsanity.com/paper/2308.10993/) (2023)
- [Integration of LSTM Networks in Random Forest Algorithms for Stock Market Trading Predictions](https://mlsanity.com/paper/2512.02036/) (2025)

## Abstract (arXiv)

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.