The Nexus Between Fiscal Deficit and Inflation in Mozambique: ARDL Model Approach
Bibliographic record
Abstract
This study investigates the relationship between fiscal deficits and inflation in Mozambique from January 2017 to December 2023 using an Autoregressive Distributed Lag (ARDL) model. Monthly data on inflation, money supply, and interest rates were collected from official sources, while annual fiscal deficit and public debt figures were converted into monthly values. The Phillips-Perron (PP) test was applied to assess stationarity, the ARDL bounds test examined long-run relationships, and the Error Correction Model (ECM) captured short-run dynamics. The results confirm a significant long-run relationship between fiscal deficits and inflation, with a 1% increase in the fiscal deficit leading to a 0.0089% rise in inflation. Money supply strongly influences inflation, while public debt exhibits a negative long-run relationship. These findings highlight the importance of coordinated fiscal and monetary policies to ensure macroeconomic stability in Mozambique.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".