Does Financial Structure Matter for the Information Content of Financial Indicators?
Bibliographic record
Abstract
Of particular concern to monetary policy-makers is the considerable unreliability of financial variables for predicting GDP growth and inflation. As Stock and Watson (2003) find, some financial variables work well in some countries or over some time periods and forecast horizons, but the results do not show any clear pattern. This may be caused by the changing nature of financial structures within countries across time, or the differing types of financial structures across countries. The authors assess the extent to which financial structure across countries influences the information content of financial variables for predicting real GDP growth and inflation. Their assumption is that financial asset prices will dominate financial quantities in economies with highly developed market-based financial systems. The authors use standard methods to determine the predictive content of common financial asset prices and quantities for 29 countries. They find no systematic pattern between financial structure and whether financial asset prices or quantities are the best financial indicators for monetary policy. Importantly, financial quantities are sometimes the best financial indicator, even in economies with highly developed market-based financial systems. The authors conclude that it would be difficult to tell, a priori, whether a financial asset price or quantity would be the best indicator for monetary policy for a particular country at a particular point in time.
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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 | 0.011 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".