Do fiscal shocks explain bond yield in high- and low-debt economies?
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Bibliographic record
Abstract
Purpose The purpose of this study is to investigate how the uncertainty associated with fiscal policy, i.e. government expenditure and tax revenues, can affect the interest rates in a group of eleven countries, comprising high- and low-debt countries: Cyprus, Greece, Ireland, Italy, Portugal, Spain, Australia, Canada, Denmark, New Zealand and Norway. Design/methodology/approach The empirical analysis makes use of the structural VAR (SVAR) methodological approach, which allows us to decompose the effects of the contribution of shocks generated by each variable, as well as their transmission effects. Findings The empirical findings suggest that both demand and supply factors influence interest rates across their frequency spectrum. For the majority of high-debt countries, the course of the yields on their government bonds is driven mainly by supply side factors and not demand (i.e. government expenses or taxes) factors. Research limitations/implications Given that the economies of certain (mostly small) countries are affected by economic conditions in large countries, especially when they have large capital flows or trade much with these countries, the future empirical analysis could also consider both domestic and international (control) macroeconomic variables to explain the course of interest rates due to fiscal changes. Originality/value The previous literature does not capture the financial crisis period, nor does it take a comparative approach – high debt versus low debt – to investigate the effect of fiscal shocks on interest rates. Thus, we aim to respond to the following questions: (1) How do fiscal shocks affect interest rates in the sample of selected countries? (2) How different is the impact of fiscal shocks on interest rates in high- and low-debt countries?
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it