Testing the UIP Hypothesis-Using Data from Partially Dollarized Developing Countries
Bibliographic record
Abstract
Unlike studies relying on data from industrial countries, recent studies using data from partially dollarized developing countries have found a favorable result to the uncovered interest parity hypothesis (UIP). In this paper, we test the robustness of these results using data from partially dollarized countries with higher and more volatile inflation rate (Tanzania and Uganda) than the countries covered in previous literature on developing countries. We find that UIP does not hold in Uganda and Tanzania. In fact, unlike previous studies on developing countries, we find that the currency with the higher interest rate appreciates, i.e., there is a forward premium puzzle. We also find that the coefficients will be less biased if we use international dollar interest rates rather than domestic ones. This tells us that capital controls do not play much of a role in these countries. In addition, we test whether the higher liquidity of the currencies of trading partners will improve results in favor of UIP. The results do not provide a clear conclusion. We find less bias when using the Kenyan shilling and more bias when using the South African rand compared to the U.S. dollar.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".