Data forecasting performance evaluation of threshold spatial vector autoregressive with exogenous variables
Bibliographic record
Abstract
One time series model developed to predict economic data is Spatial Vector Autoregressive with Exogenous Variables (SpVARX). This model can accommodate simultaneously the interrelationships between variables, the impact of exogenous variables, and Inter-regional linkages. However, this model has not adjusted the nonlinearity relationships between variables. The relationship between economic variables is usually not linear. Therefore, we introduce the Threshold Spatial Vector Autoregressive with exogenous variables (TSpVARX). This paper assesses the forecasting performance of TSpVARX and compares it with SpVARX models. We conducted a simulation study by generating 100 times the simulation data with twelve scenarios. We found that the forecasting performance of the TSpVARX model is better than SpVARX when there is a nonlinear relationship between variables. In addition, we find that the forecasting performance of TSpVARX models will improve as the sample size increases.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".