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Record W2066883011 · doi:10.1080/00036846.2014.962228

Monetary policy synchronization in the ASEAN-5 region: an exchange rate perspective

2014· article· en· W2066883011 on OpenAlexaff
Hem C. Basnet, Subhash C. Sharma, Puneet Vatsa

Bibliographic record

VenueApplied Economics · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsDepreciation (economics)IndonesianExchange rateEconomicsCurrencyMonetary policyDevaluationInflation (cosmology)International economicsMonetary economicsEconomic growth

Abstract

fetched live from OpenAlex

In light of the long-standing vision of economic and monetary integration in the ASEAN (Association of Southeast Asian Nations) region and the importance of coordinating monetary policies to achieve it, the objective of this article is to assess the monetary policy synchronization among the founding members of the ASEAN, that is, Indonesia, Malaysia, the Philippines, Singapore and Thailand. Due to the importance of exchange rate movements to monetary policies, we approach this issue from a currency exchange rate perspective. Specifically, multivariate trend–cycle decomposition is employed to investigate common trends and common cycles among the exchange rates of these countries during the period 1976–2012. Our analysis reveals that the real exchange rates of Malaysia, the Philippines, Singapore and Thailand share common cycles in the short term and have common trends in the long term, but the Indonesian currency does not share these relationships. Thus, our results augur well for the synchronization of monetary policies among Malaysia, the Philippines, Singapore and Thailand. In contrast, the relatively turbulent dynamics of the Indonesian rupiah evident in frequent bouts of stark depreciation separated by periods of steady depreciation over the past three decades raise questions regarding the readiness of Indonesia for participating in a monetary alliance with the ASEAN-4 nations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations9
Published2014
Admission routes1
Has abstractyes

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