Role of financial development in economic growth in the light of asymmetric effects and financial efficiency
Bibliographic record
Abstract
Abstract The growth effects of financial development might be asymmetric and nonlinear according to the level of financialization of countries. As a corollary to this notion, in the subject study, we developed a three‐regime threshold autoregressive distributed lags (TARDL) model, which allows us to accommodate the asymmetric effect of financial development on economic growth in top 10 financially developed countries. We augmented the TARDL model by including trade openness, capital formation and labour as potential determinants of economic growth. The empirical findings revealed the existence of threshold asymmetric co‐integration between variables. In particular, in the upper regime, financial development boosts economic growth in Singapore while it exerts a negative impact on economic growth in Finland. In the middle regime, financial development increases economic growth in Australia and Singapore. However, in the lower regime, financial development hampers economic growth in the US, Malaysia and Singapore. Trade openness has a positive long‐run influence on economic growth in Canada, South Africa, Australia, Malaysia, New Zealand, Singapore, Finland and Norway. Capital formation strengthens economic growth in the US and Malaysia in the long‐run. Labour is found to sustain economic growth in the long‐run for Malaysia and Singapore. The dynamic multipliers which depict the response path of economic growth to a one‐unit shock of financial development in the three regimes highlight the discrepancies in the reaction of economic growth to financial development shocks occurring in different regimes. Important policy implications can be instigated from the empirical results.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".