Pooled Mean Group Estimation of the Bilateral Trade Balance Equation: USA <i>vis‐à‐vis</i> her Trading Partners
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Bibliographic record
Abstract
The autoregressive distributed lag model (ARDL), even though it distinguishes between the short run and the long run effect, allows both the intercepts and slopes to vary across countries. Static panel estimations, such as fixed-effects estimation (FE), cannot distinguish between the short run and the long run behavior. To address the issue of short run heterogeneity as well as long run homogeneity of the estimated coefficients in a panel framework, the pooled mean group (PMG) estimator has gained popularity since 1999. In this paper, we estimate the bilateral trade balance model for the USA vis-a-vis her 19 OECD trading partners for the period 1973q1-2004q4 using the PMG estimator and find that PMG performs better than ARDL, FE, and MG estimators and provides significant and theoretically consistent results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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