Production Forecasting of Taiwan's Technology Industrial Cluster: A Bayesian Autoregression Approach
Bibliographic record
Abstract
Abstract This study proposes a forecasting method that combines the clustering effect and non‐informative diffuse‐prior Bayesian vector autoregression (NDBVAR) model to forecast the productions of technology industries. Two empirical cases are examined to verify the proposed method: the semiconductor industry and computer manufacturing industry in Taiwan. It is found that the NDBVAR model outperforms the other three conventional time series models including the autoregression (AR), vector autoregression (VAR), and Litterman Bayesian VAR (LBVAR) models. Moreover, the NDBVAR model also outperforms the forecast reports from leading market information providers over the past several years. The forecasting method proposed is therefore concluded to be a feasible approach for production prediction, especially for technology industries in volatile environments. Résumé La présente étude propose une méthode prévisionnelle qui combine les effets de regroupement et le non‐informative diffuse‐prior Bayesian vector autoregression model (NDBVAR) pour prévoir les productions des industries de technologie. Pour évaluer la méthode proposée, l'étude examine deux cas empiriques: les industries taiwanaises du semiconducteur et de fabrication d'ordinateur. Elle révèle que le modèle NDBVAR est plus performant que les trois modèles conventionnels en série chronologique notamment le modèle d'autoregression (AR), le modèle de vecteur d'autoregression (VAR), et le modèle Litterman Bayesian (LBVAR). L'étude montre aussi qu'au cours des dernières années, les modèles NDBVAR ont été plus performants que les rapports prévisionnels des prestataires d'informations qui dominent le marché. Elle débouche sur la constatation que la méthode prévisionnelle proposée est une approche réalisable pour la prévision de la production, en particulier pour les industries de la technologie dans un environnement volatile.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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; both teacher heads agree on what is shown here.
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".