Combining disaggregate forecasts for inflation: The SNB's ARIMA model
Dr. Marco Huwiler and Daniel Kaufmann
C22, C52, C53, E37
Swiss CPI inflation, Forecast combination, Forecast aggregation, Disaggregateinformation, ARIMA models, Missing data, Kalman filter
This study documents the SNB's ARIMA model based on disaggregated CPI data used to produce inflation forecasts over the short-term horizon, and evaluates its forecasting performance. Our findings suggest that the disaggregate ARIMA model for the Swiss CPI performed better than relevant benchmarks. In particular, estimating ARIMA models for individual CPI expenditure items and aggregating the forecasts from these models gives better results than directly applying the ARIMA methodto the total CPI. We then extend the model to factor in changes in the collection frequency of the Swiss CPI data and show that this extension further improves the forecasting performance.