The Optimal Level of Financial Growth in View of a Nonlinear Macroprudential Policy Regime Model: A Bayesian Approach
Bibliographic record
Abstract
A panel data analysis of nonlinear financial growth dynamics in a macroprudential policy regime was conducted on a panel of 10 African emerging countries from 1985–2021, where there had been a non-prudential regime from 1985–1999 and a prudential regime from 2000–2021. The paper explored the validity of the inverted U-shape hypothesis in the prudential policy regime as well as the threshold level at which excessive finance boosts growth using the Bayesian Spatial Lag Panel Smooth Transition Regression (BSPSTR) model. The BSPSTR model was adopted due to its ability to address the problems of endogeneity and heterogeneity in a nonlinear framework. Moreover, as the transition variable often varies across time and space, the effect of the independent variables can also be time- and space-varying. The results reveal evidence of a nonlinear effect between finance and growth, where the optimal level of financial development is found to be 92% of GDP, above which financial development decreases growth. The findings confirmed the Greenwood and Jovanovic hypothesis of an inverted U-shape relationship. Macroprudential policies were found to trigger the finance–growth relationship. The policy recommendation is that the financial sector should be given adequate consideration and recognition by, for example, implementing appropriate financial reforms, developing a suitable investment portfolio, and keeping spending on technological investment in Africa’s emerging countries below the threshold. Again, caution is needed when introducing macroprudential policies at a low level of the financial system.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".