Seasonal Adjustment and Forecasting of Croatian GDP under Differebt Scenarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Forecasting Croatian GDP has proved to be difficult task, not only in the past which includes war for independence, but also nowadays. A lot more outliers and irregularities were detected in the period 1990-1995 than later, when economic situation has been stabilised. In this paper we examine seasonal adjustment using two different methods: X-12-ARIMA which is said to be ad hoc method (empirically based) and TRAMO/SEATS which is model-based method. Even though empirically based methods are still dominating statistical agencies throughout the world, model based method TRAMO/SEATS has been considered as a very serious contender. We have applied both methods on Croatian GDP series and have compared obtained results. The period covered was form 1st quarter 1997 until 4th quarter 2004, fixed prices. The overall seasonal adjustment quality index for X-12-ARIMA was 2.169 and 2.085 for TRAMO/SEATS thus giving the advantage for model-based method. X-12-ARIMA used correction for trading days effect, and detected no outlier, while TRAMO/SEATS detected one outlier in 4th quarter 1997. This method has proved to be better in detection of outliers. Statistics on residuals for both methods were satisfactory, but some statistics have not been performed with X-12-ARIMA because the series was not long enough. Forecast errors were within the tolerant limits. Forecasting the future Croatian GDP is not only statistical task, but also it has some political weight. Since the Croatian approach towards EU has been slowed, GDP could be slightly lower in the first quarter of 2005 than expected. Altogether, both methods produces acceptable results since the GDP series was not difficult to adjust and to forecast, but we think TRAMO/SEATS method produced better results, both in the quality of seasonal adjustment and the quality of forecasting. TRAMO/SEATS has produced slightly higher forecast for the next three years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it