Interest Rates and the Exchange Rate: A Non-Monotonic Tale
Bibliographic record
Abstract
What is the relationship between interest rates and the exchange rate?The empirical literature in this area has been inconclusive.We use an optimizing model of a small open economy to rationalize the mixed empirical findings.The model has three key margins.First, higher domestic interest rates raise the demand for deposits, and, hence, the money base.Second, firms need bank loans to finance the wage bill, which reduces output when domestic interest rates increase.Lastly, higher interest rates raise the government's fiscal burden, and, therefore, can lead to higher expected inflation.While the first effect tends to appreciate the currency, the remaining two effects tend to depreciate it.We then conduct policy experiments using a calibrated version of the model and show the central result of the paper: the relationship between interest rates and the exchange rate is non-monotonic.In particular, the exchange rate response depends on the size of the interest rate increase and on the initial level of the interest rate.Moreover, we also show that the model can replicate the heterogeneous responses of the exchange rate to interest rate innovations in several developing economies.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".