Inflation, forecast intervals and long memory regression models
International Journal of Forecasting , Volume 18 - Issue 2 p. 243- 264
We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to 2 years. Correcting for the effect of explanatory variables, we still find fractional integration and structural breaks in the mean and variance of inflation in the 1970s and 1980s. We compare the forecasts of ARFIMAX models and ARIMAX models over the period 1984–1999. The ARIMAX(1, 1, 1) model provides the best forecasts, but its multi-step forecast intervals are too large. The multi-step forecast intervals of the ARFIMAX(0, d, 0) model prove to be more realistic.
|inflation, long memory, multistep forecasting, recursive estimation, time series|
|International Journal of Forecasting|
|Organisation||Erasmus Research Institute of Management|
Franses, Ph.H.B.F, Bos, C.S, & Ooms, M. (2002). Inflation, forecast intervals and long memory regression models. International Journal of Forecasting, 18(2), 243–264. doi:10.1016/S0169-2070(01)00156-X