THE INTERDEPENDENCE BETWEEN THE FINANCIAL SECTOR AND BUSINESS SECTOR IN ASEAN 4 COUNTRIES
Bibliographic record
Abstract
In this study, we analyze the dynamic interactions between the financial sectors and the business sectors in the ASEAN-4 countries (Indonesia, Malaysia, Thailand and Singapore). To do that, we apply the newly generalized version of the Vector Autoregressive Framework (VAR) spillover index approach proposed by Diebold and Yilmaz (2012) as our method of analysis. Based on quarterly data of each variable over the period from the first quarter of 1984 to the fourth quarter of 2015 for the ASEAN-4 countries, this study finds that: 1) Spillovers between the variables move in a diverse manner over the period of analysis for each country, 2) The variable that acts as the dominant crisis transmitter in each country is different for each country, 3) The interdependence between the variables became stronger, both within and across the countries, during the crisis period. In particular, the business sectors played a leading role during the onset of the crisis, while the financial sectors took their places as the dominant source of spillovers as the crisis deepened. 4) Credit growth in Thailand was found to be the dominant transmitter of shocks to the ASEAN-3 countries. Overall, these results suggest that the strength and movement of the spillovers between the financial and business sectors changed from time to time along with the changes that happened in the economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".