Factors influencing Federal Reserve forecasts of inflation
Bibliographic record
Abstract
Purpose This study aims to both test the asymmetric information hypothesis and explore the factors influencing the one‐ through four‐quarter‐ahead Federal Reserve inflation forecasts for 1983‐2002. Design/methodology/approach Encompassing tests are used to examine the asymmetric information hypothesis. In modeling the Federal Reserve inflation forecasts, the authors are mindful of alternative theories of inflation which emphasize such determinants as cost‐push, demand‐pull and inertial factors. Findings First, the Federal Reserve inflation forecasts embody useful predictive information beyond that contained in the private forecasts. Second, with the private forecasts controlled for, the near‐term Federal Reserve inflation forecasts make use of qualitative information, and the longer‐term forecasts are influenced by the forecasts of growth in both unit labor costs and aggregate demand as well as the preceding‐quarter inflation forecasts and monetary policy shifts. Research limitations/implications The Federal Reserve forecasts are released to the public with a five‐year lag and are currently available up to the fourth quarter of 2002. This limits the use of the most up‐to‐date forecasts desirable for this study. Originality/value The factors influencing the Federal Reserve inflation forecasts are basically those emphasized publicly by monetary authorities. This finding points to the Fed's transparency and should thus help enhance its credibility with the public. Also, our results (which shed light on the predictive information in the Federal Reserve inflation forecasts not included in the private forecasts) are of value, since they can help the Fed better predict how inflation will respond to policy actions, and they can help the public form more informative inflationary expectations.
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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 | 0.006 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".