Bibliographic record
Abstract
Abstract. Currency crises are often followed by severe recessions. This is inconsistent with the predictions of second-generation currency crisis models. In these models, devaluations are supposed to restore competitiveness and stimulate the economy. Recent work on thirdgeneration crisis models remedies this inconsistency by introducing foreign currency debt and adverse ‘balance sheet effects’. However, like their earlier second-generation cousins, these newer currency crisis models rely on multiple equilibria and exogenous sunspots to explain the actual outbreak of a crisis. Hence, while recent third-generation models improve our descriptions of currency crises, they offer little improvement when it comes to explaining them. In this paper, we address this shortcoming by introducing adaptive learning into the well known model of Aghion, Bacchetta, and Banerjee (2001). Even when equilibrium is unique in this model, we show that the ‘escape dynamics ’ of the learning algorithm produce exactly the kind of Markov-Switching exchange rate behavior that is typically attributed to sunspots. An advantage of our approach is that currency crises become endogenous, in the sense that their stochastic properties can be related to assumptions about learning and other structural features of the economy. JEL Classification #’s: F31, D83. Learning Dynamics and Endogenous Currency Crises
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".