Reconsidering Cointegration in International Finance: Three Case Studies of Size Distortion in Finite Samples
Bibliographic record
Abstract
This paper reconsiders several recently published but controversial results about the behaviour of exchange rates. In particular, it explores finite-sample problems in the application of cointegration tests and shows how these may have affected the conclusions of recent research. It also demonstrates how simple simulation methods may be used to check the robustness of cointegration tests in particular applied settings, and provides information on the potential sources of size distortion in these tests. Three case studies are presented. The first is the literature on cointegration and prediction of nominal spot exchange rates spawned by Baillie and Bollerslev (1989). The second is work on the long-run validity of the monetary model of exchange rate determination, particularly the contributions of MacDonald and Taylor (1993; 1994a). The final case study looks at the evidence presented by Kasa (1992) on common stochastic trends in the international stock market. Our results suggest that Baillie and Bollerslev's results are unaffected by finite-sample problems, but that the opposite is true for the other two case studies.
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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 | 0.040 | 0.220 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".