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Record W1945598039 · doi:10.5430/rwe.v6n3p14

A Different Perspective for Current Account Deficit Issue on Some OECD Member Countries: A Binary Panel Logit Approach

2015· article· en· W1945598039 on OpenAlexvenueno aff
Şeyma Çalışkan Çavdar, Alev Dilek Aydın

Bibliographic record

VenueResearch in World Economy · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsCurrent accountFinancial crisisContext (archaeology)LogitUnemploymentIndex (typography)MacroeconomicsEconometricsMonetary economicsExchange rate

Abstract

fetched live from OpenAlex

In this paper, we aim to analyze the possible factors, which could stimulate the probability of a financial crisis by testing the relationship between current account deficit and different macroeconomic variables by using panel logit model. For this purpose, we tried to investigate the impact of current account deficit on several macroeconomic variables such as real GDP, unemployment rate, consumer price index, rate of increase in exports, rate of increase in imports and public expenditures. In this context, we particularly selected the time period of 2005-2014 in order to concentrate on the pre-crisis and post-crisis period with the aim of investigating the potential relationship between the current account imbalances and financial crisis. To implement our objective, we examine the behaviors of macroeconomic variables in 16 developed and developing OECD member countries to analyze whether the crisis shares a common macroeconomic background. Our empirical results indicate that there is a significant positive relationship between the current account deficit and public expenditure. On the other hand, significant negative relationships have been obtained between consumer price index (CPI), unemployment rate, public expenditure and the current account deficit.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.283
GPT teacher head0.383
Teacher spread0.100 · 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 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

Citations4
Published2015
Admission routes1
Has abstractyes

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