Inflation, forecast intervals and long memory regression models
Charles S. Bos1,
Philip Hans Franses1 and
Marius Ooms2
4 April 2001
Abstract
We examine recursive out-of-sample forecasting of monthly postwar
U.S. 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 two 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.
Keywords: Long Memory, inflation, time series, recursive estimation, multi-step
forecasting.
Footnotes:
1Econometric Institute, Erasmus University, Rotterdam, and
Tinbergen Institute, The Netherlands
2 Corresponding author, Department of Econometrics and Operations Research,
vrije Universiteit amsterdam, De Boelelaan 1105, 1081 HV Amsterdam,
The Netherlands, email: mooms@econ.vu.nl
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