Bibliographic record
Abstract
Phillips (1958) in his study on England, showed the existence of a relationship between the unemployment rate and inflation, and the potential existence of an arbitrage between those two aggregates. Indeed, when the unemployment rate decreases, the costs of production of fi rms tend to increase, which results in an increase of prices. Yet, a good economic health requires a decrease in unemployment and a bearable inflation rate. Thus, it is necessary to determine the unemployment rate that allows a stable inflation, so called NAIRU (Non Accelerating Inflation Rate of Unemployment). Since the NAIRU cannot actually be observed (Blanchard (2003)), it is difficult to measure it. Several unclear estimates have been made on the american NAIRU . Thus, the present study aims to contribute to this literature by a rigorous estimation of the American NAIRU with Bayesian estimation methods including MCMC (Markov Chain Monte Carlo) in a state space univariate gaussian model. In our strategy, the latent variable (NAIRU) is obtained using a Kalman fi lter and a backward smoothing algorithm as introduced by de Jong et Shephard(1995) ; the draw of parameters is performed through a Gibbs sampling. All those steps are realized many times by MCMC simulations for convergence requirement.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".