The Impact of GDP Revisions on Taylor Rule Estimations
Why this work is in the frame
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Bibliographic record
Abstract
Along with July’s advanced estimate for second-quarter GDP, the annual revisions for previous GDP estimates were released. Revisions showed a dramatically lower path for GDP than had been previously estimated. In fact, after revisions, real GDP is now believed to still be below pre-recession levels. This deeper dip in GDP is a more accurate picture of the actual economic conditions experienced throughout the recession. Less dramatically, inflation as measured by core PCE inflation was also revised.We look at how these revisions could impact policy using what is known as the Taylor rule. The Taylor rule is one of the most common tools used to evaluate Fed policy because it suggests what the federal funds rate should be and compares it to actual rates to get some insight into monetary policy decision making. The traditional rule supposes that the Fed increases rates when inflation increases and decreases rates when the output gap gets larger (the output gap is the difference between potential and actual GDP).
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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