Preliminary and Incomplete. Do not quote. Comments welcome
Bibliographic record
Abstract
The determinants of long-term nominal interest rates have not yet been fully explained by either eco-nomic theory or empirical studies. Since long-term nominal interest rates are the sum of long-term real interest rates and inflation expectations, any macroeconomic factor that impacts expected infla-tion, real rates or both should affect long-term nominal interest rates. The objective of this paper is to examine how the dynamics of nominal bond yields is related to domestic macroeconomic funda-mentals. We consider a structural VECM where identification is achieved by imposing long-run res-trictions. A technical innovation of the paper is the identification of structural stochastic trends in a VECM including exogenous variables, which enables us to address the special features of a small-open economy like Canada. We then assess the impact of various shocks- monetary, fiscal and sup-ply shocks- on nominal bond yields. Our analysis supports the view that domestic macroeconomic policies play a determining role in the long-run dynamics of nominal bond yields. First, an unexpec-ted permanent fiscal deterioration results in large increases in long-term nominal interest rates. Se-cond, a permanent shock to inflation results in higher nominal long rates. Supply shocks, however, have no significant long-run impact on long-term nominal rates. JEL Classification: E43
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.763 | 0.641 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".