ESTIMATING DYNAMIC EULER EQUATIONS WITH MULTIVARIATE PROFESSIONAL FORECASTS
Why this work is in the frame
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Bibliographic record
Abstract
Dynamic Euler equations restrict multivariate forecasts and so can be estimated and tested using the predictions of professional forecasters. We illustrate this novel, empirical method by studying the links between forecasts of U.S. nominal interest rates, inflation, and real consumption growth since 1981. Using forecast data for both returns and macroeconomic fundamentals exploits the complete panel of forecasts from the Survey of Professional Forecasters, which yields 3,400 observations, many more than the 117 quarterly time‐series observations. Harnessing the full panel enhances precision in testing asset‐pricing models and may avoid aggregation bias. We find clear evidence for the Fisher effect but mixed evidence of a relationship between expectations of real interest rates and real consumption growth. ( JEL E17, E21, E43)
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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 | 0.000 | 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) | 0.002 | 0.007 |
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