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.
Juhro, Solikin M. and Njindan Iyke, Bernard
"FORECASTING INDONESIAN INFLATION WITHIN AN INFLATION-TARGETING FRAMEWORK: DO LARGE-SCALE MODELS PAY OFF?,"
Bulletin of Monetary Economics and Banking: Vol. 22:
4, Article 4.
Available at: https://bulletin.bmeb-bi.org/bmeb/vol22/iss4/4