Are Major US Trading Partners’ Exports and Imports Cointegrated? Evidence from Bootstrap ARDL
Bibliographic record
Abstract
A study by McNown, Sam, and Goh [2018, Applied Economics, 50(13), 1509–1521] has shown that the autoregressive distributed lag (ARDL) bounds test proposed by Pesaran, Shin, and Smith [2001, Journal of Applied Econometrics, 16(3), 289–326] may draw incorrect conclusions on the status of the cointegration test, if the ARDL bounds test is not implemented correctly. We assess the long-run relationship between US exports and imports as well as between its eight major trading partners (Brazil, Canada, China, France, Germany, Japan, Mexico, and the United Kingdom) by applying the newly developed bootstrap ARDL test by McNown et al. (2018). The results show cointegration if exports are used as the dependent variable, but not when imports are being considered as the dependent variable. This suggests that the cointegration result is sensitive to the choice of the dependent variable. We have similar findings when we examine the US and its major trading partners. No long-run relationship exists between exports and imports in the case of the US, which concurs with the finding of Fountas and Wu [1999, International Economic Journal, 13(3), 51–58]. The results also suggest that the US attempts to reduce its bilateral imbalances through targeted trade policies may not be appropriate. JEL Classification: F14, C22
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".