Futuristic Projection of Time-Varying NAIRU for Canadian Economy and Its Implication for Changes in Economic Growth
Bibliographic record
Abstract
The Non-Accelerating Inflation Rate of Unemployment stands for a rate of unemployment that will not put pressure on the economy and facilitates economic growth without any inflationary pressures.This concept originated from the Philips curve, in which if economic agents are employed at full capacity, the economy produces in the long run.If we remove the inflationary pressure, we can estimate the NAIRU, which is a better policy tool.This paper seeks to estimate the time series of NAIRU in the Canadian economy for the years between 2005 to 2022.The persistency of unemployment in the Canadian economy is analyzed via NAIRU and its fluctuations.Their contribution to economic growth is considered by breaking total unemployment to NAIRU and the Unemployment Gap, which is derived from the Philips curve.The results reflect that the equilibrium unemployment rate for the Canadian economy is around 2 percent, and according to the projection of future trends, the government and, specifically Central Bank should target this rate in order to prevent fluctuations and keep the economy in its steady state in the long run.Moreover, through the Granger causality test, it is approved that NAIRU is a granger cause of economic growth.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".