NONLINEARITY AND FRACTIONAL COINTEGRATION ANALYSIS OF THE REAL EXCHANGE RATE: EVIDENCE FROM EUROZONE, CANADA, UNITED KINGDOM AND JAPAN
Bibliographic record
Abstract
The application of fractional cointegration technique is very important in resolving the Rogoff’s (1996) puzzle of purchasing power parity (PPP). By allowing deviations from equilibrium to follow a fractionally integrated process, the fractional cointegration analysis can explain the persistence of a wider range of mean-reversion behaviour better than the standard cointegration analyses. Our empirical results which based on monthly data from January 1990 to September 2008 period illustrate that the PPP reversion exists and can be characterized by a fractionally integrated process in three out of four moneys. Using these results, we can analyze the modified GPH estimator, which has obviously lower bias. The econometric results obtained from the GPH tests hold PPP as a long-run occurrence, though significant short-run deviations from PPP can exist. Therefore, the fractional cointegration analysis permits the deviations from the equilibrium to follow a fractionally integrated process and hence captures a much wider group of research of parity or mean-reversion behavior.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".