• Solikin M. Juhro Bank Indonesia
  • Bernard Njindan Iyke Deakin University


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


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How to Cite
Juhro, S., & Njindan Iyke, B. (2019). FORECASTING INDONESIAN INFLATION WITHIN AN INFLATION-TARGETING FRAMEWORK: DO LARGE-SCALE MODELS PAY OFF?. Buletin Ekonomi Moneter Dan Perbankan, 22(4), 423 - 436.