Determinants of Demand for Automobiles in Brazil: An Econometric Analysis in the Period 2012-2017
Bibliographic record
Abstract
This paper aims to investigate the determinants of demand for automobiles in Brazil in the period 2012-2017. To this end, the research initially contemplates a historical approach that shows that the sector developed, at first, guided by the State and, subsequently, in a race by foreign automakers to build the respective production plants, until it became one of the main sectors of the Brazilian economy, whose performance directly reflects the behavior of all national production. The second step was to model the contemporary behavior of the sector, where it can be seen that some variables, such as income, price, interest rate, IPI rates, and seasonality, have relevant statistical significance and can be used to interpret demand and make forecasts about the future of the sector. An econometric estimate based on ordinary least squares with a dummy variable was used. Among the results found, the importance of the income and price effects as important determinants of demand for automobiles in Brazil in the analyzed period stands out.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".