HEGY SEASONAL UNIT ROOT TEST: AN APPLICATION ON BALANCE OF PAYMENTS IN TURKISH ECONOMY
Bibliographic record
Abstract
Many data series are often subject to seasonal movements and display regular patterns of ups and downs that recur every year in the same month or quarter. Some factors like climate, festivals, production cycle characteristics, calendar effects (such as Christmas effect in December), timing decisions (the timing of school vacations, ending of university sessions) etc. underlie such repetitive seasonal variations that might differ in magnitude from year to year even they are observed regularly (Hansda, 2012). In order to test these variations, what form of seasonality (deterministic or stochastic) exists in data worked should be determined. That is, modelling seasonality is of great importance. In this paper, it has been aimed to detect the presence of seasonal unit roots on capital and financial accounts of balance of payments by using quarterly data for the periods of 1984Q1–2014Q2 and for this aim HEGY (1990) seasonal unit root testing procedure has been utilized. The results obtained have been thought to be beneficial in determining an optimal policy on foreign economic relations.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".