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Record W2980737191 · doi:10.1016/j.bir.2019.09.003

The impact of universal banking on macroeconomic dynamics: A nonlinear local projection approach

2019· article· en· W2980737191 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBorsa Istanbul Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversité du Québec en OutaouaisUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEconomicsNonlinear systemStock marketBenchmark (surveying)Real economyMonetary economicsStock (firearms)Stock priceProjection (relational algebra)EconometricsComputer scienceSeries (stratigraphy)

Abstract

fetched live from OpenAlex

We analyze the impact of universal banking on the real economy, by comparing the performance of a benchmark linear VAR model with a nonlinear projection process (Jordà, 2005) which tracks shocks asymmetries. We divide bank shocks into two categories—i.e., credit (loans) and fee-based shocks—and show that, based on U.S. data, fee-based shocks seem to have a significant feedback effect on real GDP and on the stock market. Bank fee-based activities are more sensitive to external shocks than traditional business lines (loans) and, more importantly, they are responsible for most of the feedback effects from the banking sector to the real economy and stock markets—especially during crises. Our results also indicate that in normal times, even if nonlinearities are less at play, the feedback effects remain significant, particularly since the 1997 regulatory changes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.927

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.253
Teacher spread0.220 · 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