Shocking the economy from 1967 up to 2023: reinforcing the relevance of Divisia money in US monetary policy
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Using US quarterly data (1967–2023), including inflation’s post-pandemic surge and decline alongside monetary policies characterized by quantitative easing before refocusing on the 2% target, we utilize traditional and novel econometric tools to assess the stability of key macroeconomic variables’ responses to monetary shocks. Our findings confirm the relevance of a broad Divisia aggregate in understanding monetary policy transmission and highlight its empirical importance in explaining output and price dynamics across decades. Time-varying impulse response functions (IRFs) reveal consistent and puzzle-free price responses to Divisia-based monetary shocks throughout the sample, aligning with theory. Time-varying IRFs indicate that pandemic-related outliers in GDP (2020Q2) do not disrupt results. In contrast, Fed Funds rate or shadow policy interest rate shocks often yield puzzling outcomes across earlier and extended periods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it