Bibliographic record
Abstract
In this paper we aim at testing for the Granger causality test between real GDP and unemployment in Sweden. We model a VAR (4) model on Swedish two macro-economic variables, namely, the gross domestic product (GDP) and unemployment (Un) for the period 1993:Q1 – 2011:Q2. Our main aim is to supporting further empirical evidence so as to identify the relationship between the GDP and unemployment in terms of females, males and total unemployment, with special reference to Sweden. A Granger causality test is used. The test shows that it is the GDP Granger that causes unemployment but not the other way around. An econometric model is deployed and developed on the basis of Okun’s Law. Total unemployment, male unemployment and female unemployment coefficients of the relationship between the GDP and unemployment coefficients are diverted from Okun’s coefficient and they are found to be approximately 8 per cent and statistically significant for Sweden. This stayed almost steady over time. This result also has important implications for determining macroeconomic policy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".