Signal Extraction from the Philippine National Accounts Statistics using ARIMA Model-based Methodology
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
The state of the art in signal extraction gradually evolved from the use of a mechanical form of moving average filters to the present sophisticated model-based techniques capable of performing automatic modeling and signal extraction involving hundreds or even thousands of time series in one production run. The leading edge of technology is being shared by two ARIMA model-based systems: ARIMA X12 of the US Bureau of Census and the twin programs TRAMO-SEATS developed at the Bank of Spain. These specialized expert systems have been adopted by most statistical agencies of advanced OECD countries and the European community. The Philippines on the other hand is using the ARIMA X11 system modified by the Bank of Canada in its routine seasonal adjustment and time series decomposition tasks. This study is an attempt to implement the ARIMA model-based (AMB) approach of extracting unobserved signals from 194 quarterly national accounts statistics of the Philippines using the TRAMO-SEATS system in a fully automatic modeling mode. The successful result of the application adequately demonstrates the feasibility of adopting a system being used routinely by countries in more advanced economies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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