Impacts of Dynamic Fiscal Multipliers on Brazilian Economic Growth: A Note on NARDL
Bibliographic record
Abstract
Analyzing fiscal multipliers reveals that the impacts of government spending on the economy aren’t always symmetrical; that is, an increase in spending can have a different effect than a reduction of the same magnitude. Evidence of asymmetries in the positive and negative effects of expenditures is crucial for effective fiscal policy formulation. The study analyzed the asymmetric effects of dynamic fiscal multipliers, government revenues, and expenditures on the growth of the Brazilian economy from 2003 to 2019. A Nonlinear Autoregressive Distributed Lag (NARDL) model was used, yielding the following results. A decrease in government expenditures produced an uncommon outcome. Specifically, a reduction in expenditures led to a decrease in GDP growth until the 4th quarter. After this point, GDP began to grow, moving into positive territory and remaining there until reaching its long-run negative limit in the 16th semester. In other words, a reduction in expenditures resulted in an improvement in the expectations of economic agents (producers and consumers) and in the country’s macroeconomic situation. Conversely, a reduction in revenues, through a decrease in taxes, led to GDP growth until the twelfth quarter, peaking in the fourth quarter. It can be assumed that this growth was caused by increases in households’ disposable income, enabling greater consumption, and by increased financial resources allocated to business investments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".