臺灣主要貿易預測之績效評析－以中華經濟研究院、行政院主計總處與中央研究院經濟研究所為例
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 trade forecast for Taiwan conducted by the Chung-Hua Institution for Economic Research (CIER), the Directorate-General of Budget, Accounting and Statistics (DGBAS), and the Institute of Economics, Academia Sinica (lEAS) has received considerable attention from decision makers in the private and public sectors. We evaluate the forecasting performance of the three institutions in terms of the conventional criteria and the usefulness tests recently developed by Lin et al. (2011). More specifically, we analyze the samples for the annual and quarterly projections released by CIER, DGBAS and IEAS from 1996 to 2010. Our findings are as follows. First, the directional accuracy statistics show that the one-year-ahead annual projections are generally well produced. Second, Ashley's usefulness statistics indicate that the current-quarter forecasts released by those institutions perform the best. In addition, based on the tests for usefulness (Lin et al., 2011), the annual forecasts have also done a good job. Overall, the current year forecasts (prepared in the middle of the same year) produced by DGBAS and the next-year forecasts (prepared at the end of each year) produced by IEAS perform the best. Meanwhile, the current-year forecasts for the changes in trade between Taiwan and specific countries produced by CIER also provide useful information.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.040 |
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