Bibliographic record
Abstract
This paper characterises the financial cycle in Luxembourg using both the growth and classical cycle definitions. We implement both a frequency-based approach -using band-pass filters- to measure the growth cycle and a turning-point approach to capture the classical cycle. The financial cycle is characterized using varibales related to domestic credit and asset prices. We identify the dates of peaks/troughs for growth and classical cycles, describe the characteristics of cycle phases and analyze the synchronisation between cycles for each macro-financial variable considered and the real activity. Additionally, we evaluate the synchronisation of credit and house prices across the neighbouring countries, based on the medium-term classical cycle. Finally, we introduce two novel tools to monitor the evolution of the financial cycle which are intended to contribute to informing macroprudential policy. The first tool is an optimal decision rule in the form of two warning thresholds signalling growth cycle phases related to a possible classical turningpoint. The second tool is a measure of the probability of a turningpoint in the classical cycle in each quarter after a peak in the growth cycle. The tools are built on the lead/lag relationships between peaks and troughs of growth and classical cycles. A composite index of the growth cycle is proposed as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".