MétaCan
Menu
Back to cohort
Record W250839531

Forecasting Inflation for Inflation-Targeted Countries: A Comparison of the Predictive Performance of Alternative Inflation Forecasting Models

2012· article· en· W250839531 on OpenAlexaboutno aff
Unro Lee

Bibliographic record

VenueScholarly Commons (University of the Pacific) · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsInflation targetingEconomicsMonetary policyInflation (cosmology)UnivariateAutoregressive integrated moving averageVolatility (finance)Emerging marketsEconometricsMacroeconomicsReal interest rateMonetary economicsTime seriesMultivariate statisticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

AbstractTwenty-six industrialized and emerging countries have adopted inflation targeting monetary policy since 1990 to contain escalating inflation rate. If both the level and volatility of inflation rate have diminished perceptibly for these countries since their adoption of inflation targeting policy, as evidence overwhelmingly suggests, then the predictive performance of inflation forecasting models should have improved unambiguously for these countries after they adopted inflation targeting policy. Furthermore, inflation forecasts generated by a time-series model should be more accurate than those generated by either a structural model or a naive model after the adoption of inflation targeting policy. In this study, the predictive performance of three alternative inflation forecasting models - univariate time-series (ARIMA) model, Phillips curve model, and naive model - are carefully evaluated for a selected number of inflation-targeted countries. It is found that these models generate more accurate forecasts of inflation rate for the period following the adoption of inflation targeting policy. Furthermore, out-of-the-sample inflation forecasts generated by an ARIMA model are found to be more accurate than those generated by the other two forecasting models for most countries, especially for the period following the adoption of inflation targeting policy.Keywords: Inflation targeting, inflation forecasting models, predictive performance comparison.JEL codes: C53, E31(ProQuest: ... denotes formulae omitted.)IntroductionTwenty-six industrialized and emerging countries (8 industrialized and 18 emerging countries) have adopted inflation targeting monetary policy since 1990 to combat persistently high inflation rates and inflation volatility. The first country to formally adopt an inflationtargeting policy was New Zealand (1990), which was followed by Canada (1991), Chile (1991), Israel (1992), United Kingdom (1992), Peru (1994), Australia (1994), and Sweden (1995)1. Eighteen other countries have adopted inflation targeting policy since 19952. Given the success that many of these countries have experienced in containing persistently high inflation rate, it is widely anticipated that other countries will soon adopt inflation targeting policies3.Inflation targeting monetary policy accords either the government and/or the central bank the authority to assign an explicit numerical target for the inflation rate and implement an appropriate monetary policy to achieve its inflation target4.The proponents of this policy have long claimed that inflation targeting would not only reduce inflation rate, inflation volatility, output volatility, and interest rates, but also enhance both the transparency and accountability of the monetary policy. The central bank with an explicit inflation target has to regularly publish and disseminate reports stating the bank's forecast of future inflation rate based on its outlook on the economy, the rationale for the target chosen, and the specific nature of the monetary policy to be implemented to achieve the target. Subsequently, the central bank must periodically issue reports providing an objective assessment of the success (or lack thereof) the bank has experienced in its attempt to meet the target it has chosen. Therefore, in such an environment, the central bank's decisions and the outcome of its decisions will be monitored closely by both the government and media.Empirical evidence on the merits of inflation targeting policy, however, remains somewhat inconclusive. Bernanke, Laubach, Mishkin, and Posen (1999) and Mishkin and Schmidt-Hebbel (2007) found that inflation targeting reduces inflation rate, inflation volatility, interest rates, and output growth volatility for all countries that adopted this strategy. Specifically, Mishkin and Schmidt-Hebbel showed that the average inflation rate for inflationtargeting countries has dropped from 12. …

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.119
GPT teacher head0.225
Teacher spread0.105 · 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 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

Citations4
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueScholarly Commons (University of the Pacific)Same topicMonetary Policy and Economic ImpactFrench-language works237,207