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Research questionHow can corporate distress be forecast from high-dimensional, mixed-frequency data with right-censored outcomes?Distress data may show that a firm has not failed by the study’s end without revealing its eventual event time. Numerous predictors sampled at different frequencies further complicate estimation and uncertainty quantification.
Business
Economics
Finance
Machine Learning
Research Paper
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.High-dimensional censored MIDAS logistic regression for corporate survival forecastingThe paper studies right-censored corporate survival forecasting with high-dimensional mixed-frequency predictors. It proposes a censored MIDAS logistic regression with inverse-probability weighting, sparse-group regularization, and de-sparsified inference, supported by theoretical results, Monte Carlo simulations, and an application to Chinese-listed firms. The implementation is available in the R package Survivalml.research paper · Sep 4, 2026
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