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Record W2016025121 · doi:10.1080/09535314.2012.684345

REAL-FINANCIAL LINKAGES IN THE CANADIAN ECONOMY: AN INPUT–OUTPUT APPROACH

2012· article· en· W2016025121 on OpenAlexaffabout
Danny Leung, Oana Secrieru

Bibliographic record

VenueEconomic Systems Research · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsRoyal Military College of CanadaStatistics Canada
Fundersnot available
KeywordsSocial accounting matrixEconomicsShock (circulatory)FinanceFinancial analysisInvestment (military)Financial crisisFinancial ratioFinancial intermediaryDemand shockFinancial regulationMacroeconomicsComputable general equilibrium

Abstract

fetched live from OpenAlex

The recent financial crisis highlighted the importance of better understanding the interaction between macroeconomic and financial conditions. In this paper, we provide a financial social accounting matrix for the Canadian economy and use it to assess the strength of real-financial linkages by calculating and comparing multipliers with and without endogenous financial flows. It is found that taking into account financial flows increases the impact of a final demand shock on output by 4–11%. Moreover, between 2008 and 2009H1, the investment decisions of financial institutions together with the fact that non-financial institutions were unwilling or unable to increase their financial liabilities led to estimated declines in all GDP multipliers. The impact of a final demand shock on GDP declined 3–5%, while the impact of an increase in the availability of investment funds fell 30% and 55% for financial and non-financial corporations, respectively.† †The views expressed in this paper are those of the authors. No responsibility for them should be attributed to Statistics 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.151
GPT teacher head0.328
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations20
Published2012
Admission routes2
Has abstractyes

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