MétaCan
Menu
Back to cohort
Record W2115506489 · doi:10.1017/s1365100514000340

A NOTE ON LEVERAGE AND THE MACROECONOMY

2014· article· en· W2115506489 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.

Bibliographic record

VenueMacroeconomic Dynamics · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDeleveragingLeverage (statistics)Financial crisisFinancial intermediaryShadow banking systemMonetary economicsEconomicsFinancial systemBusinessMacroeconomicsComputer science

Abstract

fetched live from OpenAlex

In this paper we investigate the relationship between leverage and the level of economic activity in the United States, using quarterly data over the period 1951–2012. We address the question for five different measures of leverage—household leverage, nonfinancial firm leverage, commercial bank leverage, broker–dealer leverage, and shadow bank leverage—making a distinction between traditional banks and shadow banks, the latter being a consequence of financial innovation and deregulation in the financial services industry over the past 30 years. We investigate whether the relationship between leverage and the level of economic activity is nonlinear and asymmetric using slope-based tests as well as tests of the null hypothesis of symmetric impulse responses. Our results inform policymakers about the important distinction between traditional banks and the market-based financial intermediaries that have been at the center of the global financial crisis of 2007–2009. They also inform about the macroeconomic effects of the deleveraging process that began in 2008, as well as about the need for countercyclical macroprudential policies to reduce the procyclicality of the financial system.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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