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Inflation, Forecast Intervals and Long Memory Regression Models

Author

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  • Charles S. Bos

    () (Erasmus University Rotterdam)

  • Philip Hans Franses

    () (Erasmus University Rotterdam)

  • Marius Ooms

    () (Vrije Universiteit Amsterdam)

Abstract

We examine recursive out-of-sample forecasting of monthly postwarU.S. core inflation and log price levels. We use theautoregressive fractionally integrated moving average model withexplanatory variables (ARFIMAX). Our analysis suggests asignificant explanatory power of leading indicators associatedwith macroeconomic activity and monetary conditions forforecasting horizons up to two years. Even after correcting forthe effect of explanatory variables, there is conclusive evidenceof both fractional integration and structural breaks in the meanand variance of inflation in the 1970s and 1980s and weincorporate these breaks in the forecasting model for the 1980sand 1990s. We compare the results of the fractionally integratedARFIMA(0,d,0) model with those for ARIMA(1,d,1) models withfixed order of d=0 and d=1 for inflation. Comparing meansquared forecast errors, we find that the ARMA(1,1) model performsworse than the other models over our evaluation period 1984-1999.The ARIMA(1,1,1) model provides the best forecasts, but itsmulti-step forecast intervals are too large.

Suggested Citation

  • Charles S. Bos & Philip Hans Franses & Marius Ooms, 2001. "Inflation, Forecast Intervals and Long Memory Regression Models," Tinbergen Institute Discussion Papers 01-029/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20010029
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    References listed on IDEAS

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    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
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    3. Philip Hans Franses & Marius Ooms & Charles S. Bos, 1999. "Long memory and level shifts: Re-analyzing inflation rates," Empirical Economics, Springer, vol. 24(3), pages 427-449.
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