Structural breaks, ARIMA model and Finnish inflation forecasts

Via the use of the rolling regression technique and a specific procedure fo r analysing strong structural breaks in a univariate time series model, we forecast the rate of future inflation in Finland for the time period of unr egulated financial markets since the beginning of 1987. The identified stru ctural changes in the data generating process (DGP) of inflation are labell ed with both economic events and changes in the main leading inflation indi cators. The final intervention model yields, in some cases, better forecast s than the pure rolling regression technique without identification of the strong breaks. When comparing the obtained forecasts with certain noncontin uous time series based on inflation expectation surveys with respect to act ual 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 for ecasts by the Research Institute of the Finnish Economy, the recursive proc edure also produces more accurate forecasts. (C) 2001 International Institu te of Forecasters. Published by Elsevier Science B.V. All rights reserved.