INTEREST RATES, MONEY, AND ECONOMIC ACTIVITY
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we are motivated by the fact that little is known about the relative performance of broad and narrow Divisia monetary aggregates, and by recent work that tests and rejects the appropriateness of the aggregation assumptions that underlie the various monetary aggregates published by the Federal Reserve as well as a large number of monetary asset groupings suggested by earlier studies. We present a comprehensive comparison of narrow versus broad Divisia monetary aggregates within three classes of empirical models. We compute correlations between the cyclical components of Divisia monetary aggregates at different levels of aggregation and the cyclical component of industrial production. We test for Granger causality running from the Divisia aggregates to industrial production and various other measures of real economic activity. We also reestimate a structural vector autoregression based on earlier work by Leeper and Roush [(2003) Journal of Money, Credit, and Banking 35, 1217–1256] and Belongia and Ireland [(2015) Journal of Business and Economic Statistics 33, 255–269; (2016) Journal of Money, Credit and Banking 48, 1223–1266], modifying that earlier work using monthly rather than quarterly data and extending it, both using broad as well as narrower Divisia monetary aggregates and by allowing for Generalized autoregressive conditional heteroskedasticity (GARCH) behavior in the structural shocks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.012 |
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 it