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Record W2115380289 · doi:10.7202/600927ar

Le rendement des obligations provinciales et l’incertitude politique : une analyse de séries chronologiques

2009· article· en· W2115380289 on OpenAlexaffvenueabout
Claude Montmarquette, Claude Dallaire

Bibliographic record

VenueL Actualité économique · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBondVictoryMaturity (psychological)Government (linguistics)Government bondYield (engineering)EconomicsInflation (cosmology)Value (mathematics)PaymentFinancial marketFinancial economicsMonetary economicsWelfare economicsFinancial systemPolitical scienceFinancePoliticsMathematicsStatisticsLaw

Abstract

fetched live from OpenAlex

In this text, we apply time series techniques (Box-Tiao) to isolate the influence of the Parti québécois' electoral win of November 1976 on the financial and economic costs of the Québec government borrowings. For long term bonds issue between November 1976 and February 1979, we estimated at 32.49 millions of $ at 1979 present value or 1.22% of the total amount borrowed, the supplementary financial cost. In terms of additional payments to non-Québécois holding Québec government bonds, this associated economic cost has been evaluated at 11.21 millions of $ at 1979 present value, representing .42% of total borrowings. These costs may vary with respect to inflation and exchange rates and it must be emphasized that they are based on the evolution of yield differentials between Québec and Ontario government bonds and not on their direct yields to maturity. In that respect, these supplementary costs are only relative to the situation of Ontario and it is not impossible that the Parti québécois' électoral win have displaced the lenders portfolios of Canadian provincial bonds to the benefit of the government of Ontario. Finally, approximately two years and half following the pequist victory, the financial markets have retrieved to their former structure.

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.

How this classification was reachedexpand

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
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 science0.0000.000
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 head0.049
GPT teacher head0.265
Teacher spread0.216 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2009
Admission routes3
Has abstractyes

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