Measuring the impact of monetary policy: a factor-augmented vector autoregressive (favar) approach under bayesian framework
Bibliographic record
Abstract
In this paper we provide evidence of the impact of monetary policy on a broad range of macro-economic variables for U.S, Canada, U.K., and Japan using factor-augmented vector auto regressive (FAVAR) model developed by Bernanke, Boivin and Eliasz (2003). Traditional approaches, such as vector auto regressive (VAR) models have not yielded satisfactory results because of the sparse information sets employed in these models. The recently developed FAVAR approach resolves this issue by augmenting VAR model with factors summarizing the information of a vast data set that is used by central banks in monetary policy decision making process. By using monthly data of 55 to 70 macroeconomic variables from the period starting as early as 1990 ending in 2010, we first show that the factors have additional information in summarizing the behavior of major economic variables and second that how contractionary monetary policy impacts a broad range of macroeconomic variables.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".