A Different Perspective for Current Account Deficit Issue on Some OECD Member Countries: A Binary Panel Logit Approach
Bibliographic record
Abstract
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".