The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries
Bibliographic record
Abstract
The yield curve is an important tool to assess the economic progress of a country. In this study, we examine the strength of the relationship between term spread and economic activity, and between the components of the yield curve and economic activity in the G7 countries using monthly data on yield rates and seasonally adjusted data on the industrial production index (IPI). After matching the start and end date of the IPI with the yield rates, the data used and respective time period are as follows: Canada: March-1994 to December-2018, France: January-1999 to December-2018, Germany: October-2005 to December-2018, Italy: July-2009 to December-2018, Japan: July-1994 to January-2019, the UK: January-1994 to December-2018, and the US: February-1990 to January-2019. The results show positive associations between term spread and economic activity for Canada, France, Germany, Japan, the UK, and the US. For Italy, a negative association is noted. All three empirical factors could predict economic activity for France and Germany at the 12-month horizon only. For all other horizons, the factors’ ability to predict economic activity varies. We observe that by including additional macro-finance variables such as the current economic growth rate and the 3-month yield rate to capture the term structure level effects, the relationship between term spread and economic activity becomes stronger. This implies that the usefulness of yield curve and its decomposed components for the purpose of predicting economic activity should be cautiously modelled and employed for policy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".