The macroeconomic effects of infrequent information with adjustment costs
Bibliographic record
Abstract
We extend the macroeconomic literature on Ss‐type rules by introducing infrequent information in a kinked adjustment‐cost model. We first show that optimal individual decision rules are both state and time dependent. We then develop an aggregation framework to study the macroeconomic implications of such optimal individual decision rules. In our model, a vast number of agents act together, and more so when uncertainty is large. The average effect of an aggregate shock is inversely related to its size and to aggregate uncertainty. These results contrast with those obtained with full information adjustment cost models. JEL Classification: E0,E1,E2,E3 Les effets macroéconomiques de l'information infréquente quand il y a des coûts d'ajustement. Les auteurs étendent la portée de la littérature spécialisée sur les règles de type Ss en proposant des postulats d'information infréquente et de fonction de coûts d'ajustement pliée. On montre que les règles de décision optimales des individus dépendent à la fois de l'état de l'environnement et du moment. On développe alors un cadre d'agrégation pour étudier les impacts macroéconomiques de ces règles optimales de décision. Dans ce modèle, un grand nombre d'agents agissent de concert, et optimales ce d'autant plus que l'incertitude s'accroît. L'effet moyen d'un choc au niveau global est inversement reliéà son importance et au niveau d'incertitude agrégée. Ces résultats contredisent ceux qu'on obtient dans des modèles de coûts d'ajustement avec pleine information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".