Capital Account Liberalization and Financial Stability: An Application of the Finite Distributed Lag Model
Bibliographic record
Abstract
The recurrence of financial crises in recent years has sparked renewed interest in the controversy over the implications of financial openness for the stability of the financial system. This article examines the relationship between capital account liberalization and financial stability in 31 sub-Saharan African countries for the period 1996-2015. Firstly, the study uses the Exchange Market Pressure Index (EMP) as the indicator of the degree of financial risk. Then, to determine the timing and the nature of the effect of capital account liberalization on financial stability, a finite distributed lag model is used. The estimation of long-term structural coefficients is obtained by the Fully Modified Ordinary Least Squares (FMOLS) method in panel data. The results show that liberalization of the capital account negatively affects financial stability after two years in sub-Saharan African countries. These results suggest that sub-Saharan African countries should standardize their strategies for liberalizing capital accounts and engage reforms to promote long-term capital flows that are more stable and improve the macroeconomic and institutional environment.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".