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 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.