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Record W3151451817 · doi:10.3386/w27084

Interest Rate Uncertainty as a Policy Tool

2020· report· en· W3151451817 on OpenAlexaff
Fabio Ghironi, Galip Kemal Ozhan

Bibliographic record

VenueNational Bureau of Economic Research · 2020
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsInterest rateEconometricsEconomicsMonetary economics

Abstract

fetched live from OpenAlex

We study a novel policy tool-interest rate uncertainty-that can be used to discourage inefficient capital inflows and to adjust the composition of external accounts between short-term securities and foreign direct investment (FDI). We identify the trade-offs faced in navigating between external balance and price stability. The interest rate uncertainty policy discourages short-term inflows mainly through portfolio risk and precautionary saving channels. A markup channel generates net FDI inflows under imperfect exchange rate pass-through. We further investigate new channels under different assumptions about the irreversibility of FDI, the currency of export invoicing, risk aversion of outside agents, and effective lower bound in the rest of the world. Under every scenario, uncertainty policy is inflationary.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.006

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 head0.433
GPT teacher head0.498
Teacher spread0.065 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2020
Admission routes1
Has abstractyes

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