Uncovered interest parity with fundamentals: a Brazilian exchange rate forecast model
Bibliographic record
Abstract
Introduction Forecasting the nominal exchange rate path is one of the most challenging aspects of an inflation-targeting framework. According to Bank of Brazil estimates, the pass-through from nominal exchange rate movements to inflation is around 10% in each quarter. Therefore, an accurate forecast of the nominal value of the currency is very important for the efficiency of an inflation-targeting regime. If the evaluation of the future exchange rate path can be made more precise, it may reduce the variance in output and inflation. Uncovered interest parity (UIP), which relates the expected nominal depreciation to the nominal interest rate differential, has been a popular model for exchange rate forecasting. But UIP has been questioned as an adequate tool to forecast future exchange rates because many empirical tests have found a negative correlation between exchange rate changes and the interest differential, in contradiction to what is predicted by UIP. This situation has led us to consider what can be gained and lost with other models for forecasting the exchange rate. A simple alternative is to assume that the exchange rate follows a random walk and is not cointegrated with any exogenous variable for which we have data. The expected future exchange rate therefore should be equal to the current value. This first approach, although simple and transparent, does not preclude the risk of occasional large forecast errors in the exchange rate and hence inflation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".