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Record W4206692122 · doi:10.24149/gwp56

Global Liquidity Trap

2010· article· en· W4206692122 on OpenAlexaff
Ippei Fujiwara, Nao Sudo, Tomoyuki Nakajima, Yuki Teranishi

Bibliographic record

VenueFederal Reserve Bank of Dallas, Globalization and Monetary Policy Institute Working Papers · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
FundersMurata Science FoundationMinistry of Education, Culture, Sports, Science and Technology
KeywordsLiquidity trapMarket liquidityEconomicsMonetary policyMonetary economicsTrap (plumbing)Inflation (cosmology)Zero lower boundInterest rateNominal interest rateOutput gapInflation targetingLiquidity crisisReal interest rate

Abstract

fetched live from OpenAlex

In this paper we consider a two-country New Open Economy Macroeconomics model, and analyze the optimal monetary policy when countries cooperate in the face of a "global liquidity trap" --i.e., a situation where the two countries are simultaneously caught in liquidity traps. The notable features of the optimal policy in the face of a global liquidity trap are history dependence and international dependence. The optimality of history dependent policy is confirmed as in local liquidity trap. A new feature of monetary policy in global liquidity trap is whether or not a country's nominal interest rate is hitting the zero bound affects the target inflation rate of the other country. The direction of the effect depends on whether goods produced in the two countries are Edgeworth complements or substitutes. We also compare several classes of simple interest-rate rules. Our finding is that targeting the price level yields higher welfare than targeting the inflation rate, and that it is desirable to let the policy rate of each country respond not only to its own price level and output gap, but also to those in the other country.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score1.000

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 head0.052
GPT teacher head0.261
Teacher spread0.210 · 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 designObservational
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

Citations6
Published2010
Admission routes1
Has abstractyes

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