Bibliographic record
Abstract
This paper explores the ability of a large set of RBC type models to explain aggregate US data by examining how well the rst-order conditions (FOCs) from each model t the data. Typically, the residuals from the FOC for hours worked are large in magnitude (more volatile than total hours), very highly persistent, and stay away from zero for long periods of time. This pattern suggests that standard RBC models are unable to capture the dynamics in the joint behaviour of consumption, output and hours that exists in the US data. We show that models which generate dynamic terms in the FOC for hours worked are able to capture this feature of the data by exploring a RBC model augmented by learning by doing which has been shown to have such a dynamic FOC. The results are remarkable. The residuals from the hours FOC are much less volatile than total hours and display no persistence. Less conclusive results emerge from models with habit formation in preferences which also yield dynamic FOCs for the labour input. We conclude that an additional dynamic component in the FOCs is essential to better capture the dynamics in the data and future research using the RBC structure should explore models that deliver it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".