Wei Miao, Jad Beyhum, Jonas Striaukas, Ingrid Van Keilegom
arXiv 13 Feb 2025 · Econometrics
arXiv:2502.09740 · PDF · DOI · OpenAlex · Extracted main text
This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors by employing a sparse-group penalty. We establish finite-sample bounds for the estimation error, accounting for censoring, the MIDAS approximation error, and heavy tails. The superior performance of the method is demonstrated through Monte Carlo simulations. Finally, we present an extensive application of our methodology to predict the financial distress of Chinese-listed firms. Our novel procedure is implemented in the R package 'Survivalml'.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Babii, Andrii and Ghysels, Eric and Striaukas, Jonas (2022) Machine learning time series regressions with an application to nowcasting self | 1.000 | 11 | 4 | 100% |
| 2 | van de Geer, Sara and Peter Bühlmann and Ya'acov Ritov and Ruben Dez… (2014) ON ASYMPTOTICALLY OPTIMAL CONFIDENCE REGIONS AND TESTS FOR HIGH-DIMENSIONAL MODELS | 0.969 | 11 | 3 | 91% |
| 3 | Babii, Andrii and Ball, Ryan T and Ghysels, Eric and Striaukas, Jonas (2023) Machine learning panel data regressions with heavy-tailed dependent data: Theory and application self | 0.941 | 6 | 3 | 83% |
| 4 | van de Geer, Sara (2016) Estimation and test under sparsity | 0.874 | 9 | 4 | 67% |
| 5 | Caner, Mehmet (2023) Generalized linear models with structured sparsity estimators | 0.874 | 8 | 2 | 100% |
| 6 | Han, Yuefeng and Tsay, Ruey S and Wu, Wei Biao (2023) High dimensional generalized linear models for temporal dependent data | 0.811 | 4 | 2 | 100% |
| 7 | Liang, Xiaoxuan and Cohen, Aaron and Heinsfeld, Anibal Sólon and Pes… (2024) sparsegl: An R Package for Estimating Sparse Group Lasso | 0.737 | 3 | 3 | 67% |
| 8 | van de Geer, Sara (2008) High-dimensional generalized linear models and the Lasso | 0.737 | 3 | 2 | 100% |
| 9 | Audrino, Francesco and Kostrov, Alexander and Ortega, Juan-Pablo (2019) Predicting US bank failures with MIDAS logit models | 0.737 | 3 | 2 | 100% |
| 10 | Blanche, Paul Frédéric and Holt, Anders and Scheike, Thomas (2023) On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 63 scored citations.