Smooth Transition GARCH Models in Forecasting Non-Linear Economic Time Series Data
Bibliographic record
Abstract
The need to capture the heterogeneous and volatility nature of both financial and economic time series theory and modeling their behavior in practical work have stimulated interest in the empirical modeling of variances which forms the basis for this study. In the study we augmented GARCH models with smooth transition model by dropping the assumption of autoregression of the model; necessary theoretical frame work was derived and properties of the new model established and illustrated with foreign exchange rate data from Federal Republic of Nigeria (Naira), Great Britain (Pound), Botswana (Pula) and Japanese (Yen) against United States of America (Dollar). The smooth transition GARCH model is better than the classical GARCH model as there were reduction in the variances of the augmented model; this claim is confirmed by the empirical illustration with foreign exchange data. Within the group of smooth transition GARCH model, Logistic Smooth Transition is adjudged the best as it produced the least variance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".