Exchange Rates, Capital, Output and Debt When Nominal Interest Rates are Policy Parameters
Bibliographic record
Abstract
R ecent years have witnessed a growing number of attempts to reduce inf la t ion by a pol icy of raising nominal interest rates. Since 1988, such measures have been particularly pronounced in Australia, Canada and the Uni ted Kingdom. I t is important to ask how higher interest rates can help to lower inf la t ion, what factors determine the speed and rel iabil i ty of the mechanisms involved, and, above al l, what the side-effects of such policies may be. The purpose of this paper is to explore various mechanisms through which nominal interest rates may affect the course of inf la t ion, i n the context of an open economy model where exchange rates are freely floating. The main focus of at tention is upon how output and income react, i n the short run and the long, to a change in the nominal rate of interest. This calls for an explici t treatment of the markets for capital and labour, as well as for money. None of these markets is assumed to clear instantaneously. International capital mob i l i t y, and the authorities ' abi l i ty to influence the evolution o f the nominal
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".