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Record W2187362184

Current Account Deficits, Sudden Stops, and International Reserves Accumulation

2009· dissertation· en· W2187362184 on OpenAlexfundno aff
Salem Nechi

Bibliographic record

VenueQSpace (Queen's University Library) · 2009
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
FundersQueen's University
KeywordsCurrent (fluid)Electrical engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

This dissertation addresses the causes of and policy responses to the 1990s current account crises.The first chapter explores the relative importance of external shocks as key determinants of the significant increase of foreign reserves accumulated in many emerging market economies, and provides a comprehensive framework to assess the adequacy of reserve holdings.Using the case of Mexico, I find that more than two thirds of the increase in international reserves can be replicated by a linear combination of external shocks, without an abrupt regime shift after the Tequila crisis.I also find that Mexico has historically adopted an appropriate reserves policy, with 1994 being an exception.However, under the current reserves policy, there is a positive probability of a current account crisis in the near future.In chapter Two, I investigate the optimal reserves policy.The analysis predicts an optimal level of reserves in Mexico that is considerably higher than the actual level.When I account for the possibility of a bailout by the outside world in case of a crisis, Mexico's current reserves policy is in the range of my model's predictions.The final chapter proposes a new explanation for the existence and nature of sudden stops.In my model, a sudden stop forms a necessary solution to the moral hazard problem in investment and can be rationalized as part of an optimal lending strategy in the face of asymmetric information.

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.

How this classification was reachedexpand

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.236
Teacher spread0.213 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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