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Record W3122630700 · doi:10.4337/9781839100093.00026

Changes in Central Bank Procedures During the Subprime Crisis and Their Repercussions on Monetary Theory

2020· book-chapter· en· W3122630700 on OpenAlexaff
Marc Lavoie

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2020
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMainstreamEconomicsMainstream economicsKeynesian economicsMonetary policyFinancial crisisMonetary theoryMonetary economicsMonetary basePost-Keynesian economicsPolitical scienceApplied economicsLaw

Abstract

fetched live from OpenAlex

The subprime financial crisis has forced several central banks to take extraordinary measures and to modify some of their operational procedures. These changes have made the deficiencies and lack of realism of mainstream monetary theory even clearer, as can be seen in undergraduate textbooks as well as in most macroeconomic models. They have forced monetary authorities to publicly reject some of the assumptions and key features of mainstream monetary theory, fearing that, on that mistaken basis, actors in the financial markets would misrepresent and misjudge the consequences of the actions taken by the monetary authorities. These changes in operational procedures also have some implications for heterodox monetary theory, in particular for post-Keynesian theory. My objective in this article is to analyze the implications of these changes in operational procedures for an understanding of monetary theory. I take the evolution of the operating procedures of the federal Reserve since august 2007 as an exemplar. The U.S. case is particularly interesting, both because it was at the center of the financial crisis and because the U.S. monetary system and its federal funds rate market are the main sources of theorizing in monetary economics.

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: Other · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.195
Teacher spread0.174 · 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
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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