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Record W3124735510

Do central banks respond to exchange rate movements? A Markov-switching structural investigation

2013· preprint· en· W3124735510 on OpenAlexaboutno aff
Ragna Alstadheim, Hilde C. Bjørnland, Junior Maih

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2013
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsExchange rateMonetary policyEconomicsInflation targetingVolatility (finance)Markov chainMonetary economicsEconometricsCentral bankInterest rateInflation (cosmology)Small open economyStructural breakSample (material)StatisticsMathematics
DOInot available

Abstract

fetched live from OpenAlex

Do central banks respond to exchange rate movements? According to Lubik and Schorfheide (2007) who estimate structural general equilibrium models with monetary policy rules, the answer is "Yes, some do". However, their analysis is based on a sample with multiple regime changes, which may bias the results. We revisit their original question using a Markov switching set up which explicitly allows for parameter changes. Fitting the data from four small open economies to the model, we find that the size of policy responses, and the volatility of structural shocks, have not stayed constant during the sample period (1982-2011). In particular, central banks in Sweden and the UK switched from a high response to the exchange rate in the 1980s and early 1990s, to a low response some time after inflation targeting was implemented. Canada also observed a regime change, but the decline in the exchange rate response was small relative to the increase in the response to inflation and output. Norway, on the other hand, did not observe a shift in the policy response over time, as the central bank has stayed in a regime of high exchange rate response prior and post implementing inflation targeting.

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), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.007

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 head0.070
GPT teacher head0.243
Teacher spread0.173 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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