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Record W3122150915 · doi:10.34989/swp-1998-11

Liquidity Effects and Market Frictions

2021· preprint· en· W3122150915 on OpenAlexaff
Scott Hendry, Guang-Jia Zhang

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsBank of Canada
Fundersnot available
KeywordsEconomicsMarket liquidityMonetary policyPortfolioMonetary economicsVolatility (finance)WageInterest rateInflation (cosmology)EconometricsFinancial economicsLabour economics

Abstract

fetched live from OpenAlex

The goal of this paper is to shed light on the nature of the monetary transmission mechanism. Specifically, we attempt to tackle two problems in standard limited-participation models: (1) the interest rate liquidity effect is not as persistent as in the data; and (2) some nominal variables are unrealistically volatile. To address these problems, we introduce nominal wage and price rigidities, as well as portfolio adjustment costs and monopolistically competitive firms, to better understand how each of these costs affects the size and length of the liquidity effect following a central-bank policy action. Quantitative analysis shows that including these rigidities does improve the model, to some extent at least, in the expected manner. The main findings are: (1) wage and portfolio adjustment costs are able to deepen and lengthen the liquidity effect following a monetary policy action; (2) these two adjustment costs, especially wage adjustment costs, can reduce inflation volatility; (3) price adjustment costs, at least under money-growth policy rules, cause excessive interest-rate volatility and are unable to significantly reduce inflation volatility.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.275
Teacher spread0.241 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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