Bibliographic record
Abstract
Standard models in open economy macroeconomics predict that an expansionary (contractionary) monetary policy will lead to a currency depreciation (appreciation). Models that generate this prediction include the Dornbusch overshooting model, the flexible price model, the liquidity-effect models, as well as models based on the fiscal theory. The data however reveals an interesting twist to this prediction. We study a sample of 25 industrial and 49 developing countries and find that while the nominal exchange rate does indeed tend to appreciate in response to interest rate increases in developed countries, in develping countries the effect tends to be the opposite. In particular, in 84 percent of the developing countries in our sample, the nominal exchange rate depreciates in response to an increase in the interest rate. These findings represent a puzzle for standard models. To rationalize these empirical facts, we develop a model with two liquid assets (cash and demand-deposits) in which the central bank controls the interest rate on the liquid asset. The government finances its budget deficit with inflationary finance and firms must rely on bank credit to finance their working capital. The model generates opposing effects of interest rate changes on the exchange rate -- a money demand effect, a fiscal effect and an output effect. We show that a calibrated version of the model rationalizes the opposing responses in developed and developing 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".