Structural breaks, ARIMA model and Finnish inflation forecasts

Via the use of the rolling regression technique and a specific procedure for analysing strong structural breaks in a univariate time series model, we forecast the rate of future inflation in Finland for the time period of unregulated financial markets since the beginning of 1987. The identified structural changes in the data generating process (DGP) of inflation are labelled with both economic events and changes in the main leading inflation indicators. The final intervention model yields, in some cases, better forecasts than the pure rolling regression technique without identification of the strong breaks. When comparing the obtained forecasts with certain noncontinuous time series based on inflation expectation surveys with respect to actual future inflation, we find that the comparable point forecasts from our rolling regressions perform better than the corresponding point expectation proxies from questionnaires. When compared with the performance of the forecasts by the Research Institute of the Finnish Economy, the recursive procedure also produces more accurate forecasts. (C) 2001 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved.