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

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

Notice bibliographique

RevueScholarly Commons (University of the Pacific) · 2012
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueMonetary Policy and Economic Impact
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInflation targetingEconomicsMonetary policyInflation (cosmology)UnivariateAutoregressive integrated moving averageVolatility (finance)Emerging marketsEconometricsMacroeconomicsReal interest rateMonetary economicsTime seriesMultivariate statisticsComputer science
DOInon disponible

Résumé

récupéré en direct d'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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,039

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,013
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,119
Tête enseignante GPT0,225
Écart entre enseignants0,105 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2012
Routes d'admission1
Résumé présentoui

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