Epidemiological expectations and consumption dynamics
Bibliographic record
Abstract
In this paper, we propose an alternative theory of consumption that is consistent with excess sensitivity and smoothness of aggregate consumption. At the same time, consumption of individual households follows a random walk as in Dynan (2000). The model is based on the assumption that consumers’ expectations are not completely up-to-date at every instant of time. Our formalization follows the recent literature on modeling in‡ation expectations (Roberts (1998), Mankiw and Reis (2003)). We show that the degree of serial correlation in aggregate consumption growth is an approximate measure of the fraction of the population that does not update its macroeconomic expectations in any given period. Our point estimates indicate a highly statistically signi...cant serial correlation coe¢cient in the range of 0.7 to 0.8 in quarterly aggregate U.S. data. This would imply that approximately 25% of households are up-to-date in their information set in any given quarter.
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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 | 0.001 | 0.000 |
| 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".