Bibliographic record
Abstract
We provide a general equilibrium model with optimizing agents to compute the natural rate of interest for the G7 countries over the period 2000 to 2017. The model is solved for the equilibrium natural rate of interest, which is determined by a parsimonious equation that is easily computed from raw observable data. The model predicts that the natural rate depends positively on the consumption - leisure growth rates gap, and negatively on the capital -labor growth rates gap. Given our computed natural rate, the short-term nominal interest rates in the G7 have been higher than the natural rate since 2000, except for Germany and the U.S. during the period 2009-2017. In addition, the data do not support the prediction of the Wicksellian theory that prices tend to increase when the short-term nominal rate is lower than the natural rate. Projections of the natural rate over the period 2018 to 2024 are positive in Germany, Italy, Japan, and the U.K. and negative in Canada, France, and the U.S. The model predicts that fiscal expansion is an expensive policy to achieve a 2 percent inflation target when the Zero Lower Bound (ZLB) constraint is binding.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".