Relationship Between Financial and Real Sectors: Implications for Stable Economic Development (Evidence from Thailand)
Bibliographic record
Abstract
The real sector of an economy is the key section as activities of this sector persuade economic output and is represented by those economic segments that are essential for the progress of GDP of the economy. The sector generates better outcomes if accompanied with a healthier financial system; thus, advancement of financial sector is a means for the growth of real sector. The study in this paper explores the relationship between financial and real sectors of Thailand with the volatility analysis of GDP caused by development of financial market. The GARCH Model, Johansen-Juselius (1990) co-integration test, vector error correction model (VECM), and Granger causality testing approach was employed on time series data over the first quarter of year 1993 until the second quarter of year 2017. Consistent with past studies, both the elements of capital market (i.e. bonds and stock markets) and the money market (i.e. credit to private sector by banks) bears a positive relationship to the GDP, our results shows that both markets help promoting economic growth. We can infer that differences in financial markets’ composition and institutions do matter, as these three major sections – bond market, stock market, and banks– do not simultaneously develop and grow, but at a different level of their growth they complement each other. Our findings suggest that there exists inter dependency between real and financial sector’s technologies which in turn enlightens the effect of financial market development on the GDP growth.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".