Budget Deficits and Economic Growth: A Vector Error Correction Modelling of South Africa
Bibliographic record
Abstract
The primary motivation behind this study was to explore the consequential effects of budget deficit on South Africa`s economic growth. Six variables were used, namely: real GDP, budget deficit, real interest rate, labour, gross fixed capital formation and unemployment. The Vector Error Correction Model (VECM) was used to estimate the long-run equation and also measure the correction from disequilibrium of preceding periods. Using annual time series data spanning the period 1985 to 2015, empirical evidence from the study revealed that budget deficits and economic growth are inversely related. It was therefore concluded that high levels of budget deficit in South Africa have detrimental effects on the growth of the economy. The estimate of the speed of adjustment coefficient found in this study revealed that about 29 per cent of the variation in GDP from its equilibrium level is corrected within one year. The results obtained in this study are favourably similar to those in the literature and are also sustained by previous studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".