A modified generalized estimating equation approach for simultaneous spatial durbin panel model: Case study of economic growth in ASEAN countries
Bibliographic record
Abstract
This article briefly explains the simultaneous spatial durbin panel (SSDP) model. The study of the SSDP model is substantial because it can explain the interaction between geographic units, is more informative, diverse, efficient, exhaustive, and accurate in reaching conclusions that influence the policy determination. This article’s intention is to derive a parameter estimation method from the SSDP model using a modified generalized estimating equation approach, which is then used to model economic growth in ASEAN nations. This article compares the SSDP model with rook contiguity, 2-nearest neighbors, and a customized spatial weighted matrix in relation to an independent, first-order autoregressive, exchangeable working correlation structure. To model economic growth in ASEAN countries, a customized weighted matrix with first-order autoregressive and exchangeable working correlations is chosen based on the CIC value. The parameter analysis outcomes indicate: 1) it is a significant spatial dependence among ASEAN countries; 2) it is a significant simultaneous interaction among the gross domestic product (GDP) and foreign direct investment (FDI); 3) GDP has a greater influence on FDI than FDI does on GDP; 4) The economic growth is directly affected by the labor force total; and 5) trade openness has a direct effect on FDI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".