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Record W1532745392 · doi:10.34989/swp-2010-9

Alternative Optimized Monetary Policy Rules in Multi-Sector Small Open Economies: The Role of Real Rigidities

2021· preprint· en· W1532745392 on OpenAlexaffabout
Carlos de Resende, Ali Dib, Maral Kichian

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsEconomicsMonetary policyInflation (cosmology)Welfare economicsHumanitiesKeynesian economicsPhilosophyPhysics

Abstract

fetched live from OpenAlex

Inflation-targeting central banks around the world often state their inflation objectives with regard to the consumer price index (CPI). Yet the literature on optimal monetary policy based on models with nominal rigidities and more than one sector suggests that CPI inflation is not always the best choice from a social welfare perspective. We revisit this issue in the context of an estimated multi-sector New-Keynesian small open economy model where sectors are heterogeneous along multiple dimensions. With key parameters of the model estimated using data from an inflation targeting economy, namely Canada, we particularly focus on (i) the role of sector-specific real rigidities, specially in the form of factor mobility costs, and (ii) welfare implications of targeting alternative price indices. Our estimations reveal considerable heterogeneity across sectors, and in several dimensions. Moreover, in contrast to existing studies, our welfare analysis comparing simple optimized policy rules based on alternative sectoral inflation rates provides support for CPI-based targeting policies by central banks. Capital mobility costs matter importantly in this regard.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.078
GPT teacher head0.262
Teacher spread0.184 · 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

Citations18
Published2021
Admission routes2
Has abstractyes

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