Estimating quarter to quarter economic growth in Kosovo
Bibliographic record
Abstract
Estimation of economic growth in real time is one of the main objectives for most of the policymakers. At this point Gross Domestic Production at quarterly frequencies is the most accurate indicator. Most of the countries that have developed this macroeconomic indicator are publishing GDP in two main forms, seasonal and not seasonal adjusted. Seasonality is a present phenomenon for most of the economic sectors at quarterly frequencies. These different rhythms caused by weather, human habits, legislation, and so on, tend to repeat themselves periodically. It is therefore natural to try to estimate their impact and take account of them in the analysis of quarterly time series. Seasonal adjustment serves to facilitate the comparisons between periods especially in the linked periods. These adjustments tried to avoid phenomena like the increase of employment in agriculture or accommodation sector during summer because of production cycle and the increase number of tourists. In this paper it will be presented a method how to do seasonal adjustment on quarterly GDP by production approach in case of Kosovo. The paper details an application of Tramo and Seats method using, to seasonal adjustment and trend-cycle estimation. Based on sector analyses will be discussed the important problem of the choice between direct and indirect adjustment of quarterly series.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".