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Record W2024933960 · doi:10.1080/0003684042000337389

Determinants of the long-term yield in Canada: an open economy VAR approach

2005· article· en· W2024933960 on OpenAlex
Ronald H. Lange

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueApplied Economics · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEconomicsYield curveVector autoregressionTerm (time)Monetary policySmall open economyMonetary economicsYield (engineering)Interest rateOpen economyAggregate demandAutoregressive modelStructural vector autoregressionMacroeconomicsEconometricsExchange rate

Abstract

fetched live from OpenAlex

This study analyses the economic determinants of short- and long-term interest rates in Canada using a structural vector autoregressive (VAR) model. The VAR takes into consideration that Canadian financial markets are small and open relative to those in the USA and that Canada is a relatively large exporter of commodities. In part, the empirical results for Canada are similar to those for the USA. Aggregate demand shocks have relatively large and persistent effects on long-term yields, while aggregate supply shocks do not have significant effects. However, monetary policy shocks in Canada are found to have larger and more persistent effects on long-term yields than those found for the USA. The most striking result is that movements in US monetary policy have relatively large, significant and persistent effects on Canadian long-term bond yields. Furthermore, US monetary policy disturbances can account for the overall trend in long-term yields in Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.223
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it