Evaluating Exchange Rate Models Based on Rational Expectations versus Imperfect Knowledge Economics
Bibliographic record
Abstract
Understanding the factors that explain the causes of exchange rate swings has been one of the major concerns in the international finance field. Conventional models, which utilize Rational Expectation Hypothesis (REH), are frequently tested and employed in the international finance literature to explain the exchange rate fluctuations. On the other hand, Imperfect Knowledge Economics (IKE) has recently been developed as an alternative approach to understand the same concerns on the exchange rate swings. This paper, for the first time, employs the Lakatosian framework of Scientific Research Programs (SRPs) to evaluate the main theoretical contributions of these models on the exchange rate fluctuations. First, the study evaluates the novel facts that are associated with each of the Lakatosian protective belts for both of these SRPs. In addition, the study evaluates the empirical evidence of each SRP related to these novel facts, and argues whether or not these are theoretically and empirically progressive in the Lakatosian framework.
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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 | 0.007 | 0.032 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".