Financial Condition Index and interest rate settings: a comparative analysis
Bibliographic record
Abstract
In the last thirty years, there has been a widespread move towards financial liberalisation, both within and across national borders. This economic development brought researchers to investigate the link between asset prices, inflation and the conduct of monetary policy. Stating from the seminal work of Alchian and Klein (1973) it is often argued that the forward-looking nature of asset prices makes them good proxies for the information left out of conventional inflation measures. It is also widely accepted that asset price inflation developments are closely associated with general inflation trends. This paper investigates the role of asset prices in the conduct of monetary policy in United States, Canada and the Euro Area. It has two focal points. First, we construct Financial Condition Indexes for four countries using the Kalman Filter algorithm. This methodology allows us to capture the changes of the weights associated with each financial variable in explaining the output gap over time. Second, we proceed by estimating forward-looking Taylor rules augmented for FCI. Our results suggest that the Financial Condition Index enter positively and statistically significant into the FED, ECB and Bank of Canada interest rate settings. This gives a positive view for the use of the FCI as an important short term indicator to guide the conduct of monetary policy in three out of four countries analyzed.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".