Uncovered interest parity and threshold cointegration approach: theory and evidence
Bibliographic record
Abstract
Although the uncovered interest parity (UIP) condition has played an important role in many theoretical and empirical models of open-economy macroeconomics, the conventional empirical test for the validity of UP has shown that the null hypothesis of the UP condition is almost always rejected and, especially, the slope estimate of the forward premium is significantly negative. Four different approaches to explaining this UIP puzzle have been introduced so far, but none of them has succeeded in providing a fully acceptable rationale and empirical test result. The present paper investigates the UIP puzzle using the threshold cointegration approach for major four currencies: the Canadian dollar, the Japanese yen, the German mark, and the British pound. We find that the slope estimate of the forward premium in the context of the threshold vector error-correction model (TVECM) has a positive or negative sign, depending on currencies. Based on this finding, we conclude that the threshold cointegration approach does not provide robust evidence for the UIP condition, and that the UIP puzzle remains partially unsolved. However, our paper gives some contributions to the study of the UIP puzzle and the application of the threshold cointegration approach. First, we provide a general review of the threoretical and empirical studies on the UIP condition including the threshold cointegration approach. Second, we find that the spot and forward exchange rates for the four major currencies have a bivariate threshold cointegration property. Third, we estimated the band TVECM for the spot and forward exchange rates of these currencies. Fourth, we constructed out-of-sample forecasts using the TVECM and four alternative models, and found that the TVECM has the best forecasting ability based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) criteria. According to this finding, the estimated TVECM can be used as a predictor of short-term movements in exchange rates although the estimated results are inconsistent with the UIP condition.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".