The Trimmed Mean PCE Inflation Rate: A Better Measure of Core Inflation
Bibliographic record
Abstract
Abstract Over the last two decades, the Federal Reserve and numerous other central banks have placed increased emphasis on low and stable inflation as a primary goal of monetary policy. Many central banks have set out explicit numerical inflation targets, often with guidance from their country’s legislatures. Across the globe, central banks have been successful in their quest for low and stable inflation. The process began with the G-7 countries (the U.S., UK, Germany, France, Italy, Canada, and Japan) and was aided by the Maastricht Treaty, which pressured Western European countries to converge to low inflation rates if they wished to join the Euro-currency area (figure 6.1). More recently, several previously high-inflation countries (Mexico, Israel, Turkey, Spain, Portugal, and Greece, to name just a few) have joined the select circle of low-inflation countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".