FORECASTING INDONESIAN INFLATION WITHIN AN INFLATION-TARGETING FRAMEWORK: DO LARGE-SCALE MODELS PAY OFF?

Abstract

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.