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Record W2915671592 · doi:10.34989/san-2016-16

Comparing Forward Guidance and Neo-Fisherianism as Strategies for Escaping Liquidity Traps

2021· article· en· W2915671592 on OpenAlexaff
Robert Amano, Thomas J. Carter, Rhys R. Mendes

Bibliographic record

VenueStaff Analytical Notes · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsMarket liquidityEconomicsLiquidity trapMonetary economicsLiquidity risk

Abstract

fetched live from OpenAlex

What path should policy-makers select for the nominal rate when faced with a liquidity trap during which the effective lower bound binds? Conventional wisdom has generally favoured a commitment to keep rates low for long, namely under the guise of forward guidance policies, while Cochrane (2016) and others have recently made the case for neo-Fisherian policies that involve pegging rates at a high level in the hopes that the Fisher effect might deliver higher inflation over time. We compare these two options as strategies for escaping liquidity traps and argue that their relative merits likely depend on the mechanism that initially gave rise to the particular trap in question. More specifically, we argue that policy-makers should distinguish between “shock-based” traps that arise following large, negative demand shocks (Eggertsson and Woodford 2003) and “expectation-based” traps that arise from self-fulfilling shifts in private sector expectations (Benhabib, Schmitt-Grohe and Uribe 2001). This is because forward guidance likely dominates in the former case, while the latter may favour neo-Fisherianism to the extent that keeping rates low for long might reinforce the pessimistic beliefs underlying expectation-based traps. Although empirical strategies for distinguishing between these two mechanisms would be a promising topic for future research, we conclude by arguing that the shock-based mechanism likely provides a more plausible explanation for the low inflation witnessed in many developed countries during and after the Great Recession.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.123
GPT teacher head0.279
Teacher spread0.156 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes1
Has abstractyes

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