MétaCan
Menu
Back to cohort
Record W2190478647 · doi:10.1017/s1365100507070046

LEARNING DYNAMICS AND ENDOGENOUS CURRENCY CRISES

2008· article· en· W2190478647 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacroeconomic Dynamics · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEconomicsCurrencyAdaptive learningMonetary economicsMarkov chainCurrency crisisBalance sheetComputer scienceArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

This paper introduces adaptive learning into the third-generation currency crisis model of Aghion, Bacchetta, and Banerjee (2001, Currency crises and monetary policy in an economy with credit constraints, European Economic Review 45, 1121–1150). Adaptive learning might reflect, for example, uncertainty about the economy's exposure to adverse balance sheet effects. Even when equilibrium is unique, we show that the learning algorithm's escape dynamics can produce the same kind of Markov-switching exchange rate behavior that is typically attributed to sunspots or herds. An advantage of our learning model 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.035
GPT teacher head0.205
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it