Inflation, forecast intervals and long memory regression models

Inflation, forecast intervals and long memory regression models

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Article ID: iaor2005241
Country: Netherlands
Volume: 18
Issue: 2
Start Page Number: 243
End Page Number: 264
Publication Date: Apr 2002
Journal: International Journal of Forecasting
Authors: , ,
Keywords: forecasting: applications
Abstract:

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.

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