Using a Wage–Price‐Setting Model to Forecast US Inflation
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This study modifies a wage–price‐setting (WPS) model to forecast US inflation over 1‐ to 3‐year horizons, based on the assumption that firms use a rule of thumb to set prices after settling a wage agreement. The out‐of‐sample forecast results show that productivity growth is a powerful predictor of inflation, in the sense that during the 1990Q1–2023Q4 period, the modified WPS model improved upon some univariate benchmark models and multivariate models such as the Phillips curve, term spread, and wage‐inflation models. From the early 2000s to the prepandemic period, forecast accuracy was improved by combining productivity growth with anchored inflation expectations. Interestingly, during this period, forecasts derived from the WPS model with constant‐inflation expectations were found to slightly outperform Greenbook forecasts in forecasting quarter‐over‐quarter inflation from two‐ to four‐quarter horizons.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it