The Causality between Stock Market Development and Economic Growth: Econometric Evidence from Bangladesh
Bibliographic record
Abstract
The growth performance of Bangladesh over the last one and a half decades along with the stock market development sparks the question of whether stock market development has a significant impact on economic growth of the economy. The study investigates the time series evidence of the influence of stock market development on growth of Bangladesh economy for the period 1993-2016 employing ARDL Bounds testing approach and finds stock market development has direct impact on economic growth both in the short-run as well as in the long run together with financial depth, interest rate spread and real effective exchange rate. Granger causality tests confirm a bidirectional causal relationship between stock market development and economic growth. However, the study fails to identify a system convergent to equilibrium in regard to stock market development along with other factors that has important economic implications. Persistent improvement in financial depth and fall in interest rate spread throughout the sample period with consistent performance of real effective exchange rate except some spikes in recent years raise the demand for playing the needed role by all concerned for confirming the stability of stock market and its development in order to validate the steady state of equilibrium in the long-run.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".