From Boom to Bust: Unravelling the Cyclical Nature of Fiji’s Money Demand
Bibliographic record
Abstract
This study investigates cyclical asymmetries in money demand models considering the moderating effect of financial development. Prior research has overlooked this issue in the money demand literature within the Fijian context, where research is outdated. Using annual data from 1983 to 2023, we find that income elasticity is about positive unity, irrespective of recessions or expansions. In expansions, an increase in interest rates reduces money demand. An increase in interest rates reduces money demand nine times more strongly in recessions. These effects are accentuated with financial development. Declining interest rates do not impact money demand. The findings suggest that stable money demand could be achievable, but only once the impact of structural breaks is accounted for. Under ideal conditions—without such breaks—money demand exhibits stability, and its connection to income and interest rates appears predictable. However, in reality, structural disruptions complicate this relationship, making money demand less consistent with its key drivers and undermining the reliability of money supply as a monetary policy instrument. The findings align with the pulling on a string hypothesis that monetary contractions control inflation, but expansions may not impact output.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".