Bibliographic record
Abstract
ABSTRACT Factor models are indispensable tools in economics and finance, providing valuable insights into the latent structures underlying complex datasets. Nevertheless, the prevalence of heavy‐tailed macroeconomic and financial data, often characterized by extreme values and greater skewness than that found in a normal distribution, presents significant analytical challenges. This article introduces mode‐adaptive factor models (MAFM) for robust factor analysis in high‐dimensional panel data, inspired by the equivalence between conventional principal component analysis and the constrained least squares method in factor models. Unlike traditional factor models that concentrate on mean estimation, MAFM leverage the mode to capture central tendencies more effectively, particularly in the presence of skewed and heavy‐tailed distributions. To facilitate MAFM for factor analysis, we develop an iterative mode regression algorithm that integrates the expectation‐maximization procedure, ensuring convergence to a stationary solution. We establish the theoretical properties of the MAFM estimators without imposing moment constraints on idiosyncratic errors and propose a mode information criterion for consistent factor number selection. We also suggest a data‐dependent bandwidth selection procedure to enhance the flexibility of MAFM. The simulation studies demonstrate the effectiveness of MAFM across diverse distributional settings. An empirical application to macroeconomic forecasting further highlights the practical advantages of MAFM, showcasing their robustness and efficacy in real‐world analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".