Bibliographic record
Abstract
"Central bank announcements contain both: - a stance on monetary policy - an assessment of the economic outlook When expecting strong growth, central banks usually raise interest rates to stabilize the economy. But an announcement of interest rates higher than the market anticipated may change market participants’ beliefs, leading them to conclude the economy is stronger than they thought. When policy makers and researchers assess monetary non-neutrality, they must consider the distinction between the effect of the central bank’s monetary policy stance and the effects of a shift in fundamentals. I measure this distinction by constructing a monetary policy shock series and applying it to the Federal Reserve’s monetary policy announcements. In particular, I examine the high-frequency movements of interest rates in a 30-minute window around the time of the Federal Reserve’s announcement. Because the announcement may reshape expectations about future monetary policy, I use changes in contract rates of interest rate futures settled in both the current and subsequent months. I then project these surprises onto the Federal Reserve’s information set, which is measured by its own economic forecasts preceding each announcement. The monetary policy stance shock is the portion of interest rate surprises which cannot be explained by the economic forecasts. I estimate the causal effects of monetary policy on the financial market and the macro economy using the constructed monetary policy stance shock as an instrument variable. My findings are consistent with the traditional channels of monetary policy non-neutrality. A contractionary monetary policy shock will cause: - an upward revision in private forecasts of the unemployment rate - a downward revision in private forecasts of inflation - a decline in stock prices"
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".