Tracking Monetary-Fiscal Interactions across Time and Space
Bibliographic record
Abstract
https://www.ijcb.org/journal/ijcb18q2a4.htmThe long-term fiscal outlook of most high-income countries is grim. Should independent central bankers be afraid of an unpleasant monetarist arithmetic, i.e., fiscal imbalances spilling over to monetary policy and jeopardizing price stability? To provide some insights, this paper tracks the interactions between fiscal and monetary policies in the data since 1980 for Australia, Canada, Japan, Switzerland, the United Kingdom, and the United States. In doing so it uses a combination of time-varying parameter vector autoregression with sign, magnitude, and contemporaneous restrictions identification. Unlike conventional approaches, this can capture changes in monetary and fiscal behavior that are gradual and differ across the two policies. Our results show that in the United States the degree of monetary policy accommodation of fiscal shocks (debt-financed government spending) increased gradually between the late 1980s and the 2008 crisis, i.e., over the whole tenure of Chairman Greenspan. In contrast, it seems to have decreased over this period in the United Kingdom, Australia, Switzerland, and Canada. Our benchmark analysis and several robustness checks show that legislating numerical inflation targets may account for some of the country differences, presumably because they may shift the strategic power from fiscal to monetary policy. We conclude by considering the implications of our results for the long-term likelihood of an unpleasant monetarist arithmetic in the six countries.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".