Pooled Mean Group Estimation of the Bilateral Trade Balance Equation: USA <i>vis‐à‐vis</i> her Trading Partners
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‐à‐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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".