Forecasting Indonesian inflation within an inflation-targeting framework: do large-scale models pay off?

Solikin M. Juhro, Bernard Njindan Iyke

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3 Citations (Scopus)

Abstract

We examine the usefulness of large-scale inflation forecasting models in Indonesia within an inflation-targeting framework. Using a dynamic model averaging approach to address three issues the policymaker faces when forecasting inflation, namely, parameter, predictor, and model uncertainties, we show that large-scale models have significant payoffs. Our in-sample forecasts suggest that 60% of 15 exogenous predictors significantly forecast inflation, given a posterior inclusion probability cut-off of approximately 50%. We show that nearly 87% of the predictors can forecast inflation if we lower the cut-off to approximately 40%. Our out-of-sample forecasts suggest that large-scale inflation forecasting models have substantial forecasting power relative to simple models of inflation persistence at longer horizons.

Original languageEnglish
Pages (from-to)423-436
Number of pages14
JournalBuletin Ekonomi Moneter dan Perbankan
Volume22
Issue number4
DOIs
Publication statusPublished - 12 Feb 2020
Externally publishedYes

Keywords

  • Dynamic model averaging
  • Forecasting inflation
  • Inflation-targeting framework
  • Large-scale models

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