Economic Shocks and the Fed’s Policy--The Transmission Conduit and Its International Linkage
Bibliographic record
Abstract
This paper uses a Bayesian Vector Autoregression (BVAR) model to evaluate the impacts of policy and financial shocks on several economic variables.We use both expected federal funds and unexpected federal funds rates as an indicator of monetary policy respectively.In addition to the traditional financial market measures, i.e., bank loan and equity price changes to signal the financial shocks, we introduce a new credit measure that reflects the differential credit access for small and large firms. We use U.S. monthly data from July 1954 to March 2009.The results of our impulse response functions and variance decomposition provide a positive assessment of the Fed’s recent policy actions.We also extend the framework to include four foreign countries and show that foreign stock prices are significantly affected by U.S. equity shock in a later time period, indicating a stronger global integration recently.Nevertheless, the U.S. equity market is not affected much by the foreign equity market.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".