Are the Responses of the U.S. Economy Asymmetric to Positive and Negative Money Supply Shocks
Bibliographic record
Abstract
Abstract We investigate whether the United States economy responds asymmetrically to positive and negative money supply shocks of different magnitude, using a test recently introduced by Kilian and Vigfusson (Quant Econ 2:419–453, 2011) based on impulse response functions. We use quarterly data, over the period from 1967:1 to 2014:1, and the new CFS Divisia monetary aggregates, making a comparison among the narrower monetary aggregates, M1, M2M, MZM, M2, and ALL, and the broad monetary aggregates, M4+, M4-, and M3. We show that there is no statistically significant evidence of asymmetry in the response of the U.S. economy to positive and negative money supply shocks of different magnitude.(This abstract was borrowed from another version of this item.)
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".