A BVAR Note on the J-Curve and the Marshall-Lerner Condition for Brazil
Bibliographic record
Abstract
In the present work, the hypotheses of the J-curve and the Marshall-Lerner condition for Brazil from January 2003 to December 2019 were tested. The impulse-response function (IRF) and the variance decomposition (VD) of a Bayesian vector autoregressive model (Minnesota priors) served as instruments for the empirical verification of the above-mentioned hypotheses. The Bai and Perron (1998, 2003) structural break test was carried out, which identified two breaks and, consequently, three subsamples, from January 2003 to October 2007; December 2007 to June 2015; and July 2015 to December 2019. The results showed that the estimated BVAR empirically supports the hypotheses in question. In the short term, it is observed that a real depreciation of the Brazilian currency results, in the first five months, in a deficit in the trade balance. However, as of the fourth month, the result of the trade balance becomes positive, and it remains like that for longer than ten months. This means that one cannot reject the J-curve hypothesis. For a forecast horizon of 36 months, it was found that the Marshall-Lerner condition should not be rejected either. In other words, a currency devaluation causes an increase in the trade balance for longer than three years.
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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 | 0.015 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".