A Theory of Quantitative Easing Policy and Negative Interest Policy Based on the Japanese Experience
Bibliographic record
Abstract
Using a two-period overlapping-generations model, I elucidate how quantitative easing policy and negative interest policy affect an economy based on the Japanese experience under the Abe cabinet. Quantitative easing policy forces a huge amount of money hoarding. Accordingly, the rate of return for money is required to rise. This implies that disinflation and/or deflation are accelerated in Japan, which is in line with reality. On the other hand, quantitative easing policy stimulates the aggregate demand, which brings about a mild recovery in business. The business upturn tightens the foreign market because of an increase in imports and causes the home currency to depreciate.Negative interest policy implies there is a tax levied on money hoarding. Hence, as longas the government expenditure is kept constant, money circulating in an economy decreases, thereby discouraging business. Such a downturn reduces aggregate income and imports. This induces excess supply of foreign exchange. Consequently, the exchange rate appreciates to equilibrate the market.These characteristics of the business cycle in conjunction with changes in the attitude of the monetary authority are entirely consistent with the current Japanese experience under Abenomics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".