Impactos da taxa de câmbio, preços das >i<commodities>/i< e renda mundial sobre as exportações do agronegócio brasileiro entre 1997 e 2018
Bibliographic record
Abstract
Exchange rate, commodity prices and world income impacts on Brazilian´s agribusiness exports between 1997 and 2018In a world of increasingly integrated economies, exports develop a role with growing importance, bringing currency and generating income for the countries.In this context, this work aimed at investigating the main macroeconomic factors that may impact the value of exports.As explanatory variables, the exchange rate, the commodity prices and the world income were considered.Models were proposed for two distinct sectors, one of which is the agribusiness sector and the other sector encompasses the other products of the economy.To classify the exports into the agribusiness or the other sectors, the classification proposed by the Ministry of Agriculture, Livestock and Supply was employed.As an econometric approach, time series procedures were performed, such as unit root tests and cointegration tests.The agribusiness exports were found to be cointegrated and, therefore, VEC error-correction models, were employed and the long-and short-term relationship analyses were undertaken.Conversely, the export series of the other sectors were not cointegrated, thus being proposed a vector autoregressive model, VAR, and therefore the short-term analysis was performed.In general, the world income and the commodity prices were more relevant to explain the movements in exports than the exchange rate.Observing the impulse response functions, it was noticed that the effects of the shocks on the other variables are more strongly presented until the fourth month, and for the exports of the other sectors, the effects of the shocks are dissipated, whereas for the agribusiness exports, the effects remain even after twelve months.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".