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Testing and forecasting the degree of integration in the US inflation rate

  • Luis A. Gil-Alana

    (University of Navarre, Spain)

In this article we model the log of the US inflation rate by means of fractionally integrated processes. We use the tests of Robinson (1994) for testing this type of hypothesis, which include, as particular cases, the I(0) and I(1) specifications, and which also, unusually, have standard null and local limit distributions. A model selection criterion is established to determine which may be the best model specification of the series, and the forecasting properties of the selected models are also examined. The results vary substantially depending on how we specify the disturbances. Thus, if they are white noise, the series is I(d) with d fluctuating around 0.25; however, imposing autoregressive disturbances, the log of the US inflation rate seems to be anti-persistent, with an order of integration smaller than zero. Looking at the forecasting properties, those models based on autocorrelated disturbances (with d < 0) predict better over a short horizon, while those based on white noise disturbances (with d > 0) seem to predict better over longer periods of time. Copyright © 2005 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.951
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 24 (2005)
Issue (Month): 3 ()
Pages: 173-187

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Handle: RePEc:jof:jforec:v:24:y:2005:i:3:p:173-187
DOI: 10.1002/for.951
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Evans, Martin D D & Lewis, Karen K, 1995. " Do Expected Shifts in Inflation Affect Estimates of the Long-Run Fisher Relation?," Journal of Finance, American Finance Association, vol. 50(1), pages 225-53, March.
  2. Schmidt, Peter & Phillips, C B Peter, 1992. "LM Tests for a Unit Root in the Presence of Deterministic Trends," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 257-87, August.
  3. Fama, Eugene F. & Gibbons, Michael R., 1984. "A comparison of inflation forecasts," Journal of Monetary Economics, Elsevier, vol. 13(3), pages 327-348, May.
  4. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
  5. Gil-Alana, L. & Robinson, P.M., 1998. "Testing of Seasonal Fractional Integration in U.K. and Japanese Consumption and Income," Economics Working Papers eco98/20, European University Institute.
  6. Crowder, William J & Hoffman, Dennis L, 1996. "The Long-Run Relationship between Nominal Interest Rates and Inflation: The Fisher Equation Revisited," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(1), pages 102-18, February.
  7. Robert B. Barsky, 1986. "The Fisher Hypothesis and the Forecastability and Persistence of Inflation," NBER Working Papers 1927, National Bureau of Economic Research, Inc.
  8. Kim, C.J., 1992. "Unobserved-Component Time-Series Models with Markov- Switching Heteroskedasticity: Changes in Regimes and the Link between Inflation Rates and Inflation Uncertainty," Papers 92-1, York (Canada) - Department of Economics.
  9. Nelson, Charles R & Schwert, G William, 1977. "Short-Term Interest Rates as Predictors of Inflation: On Testing the Hypothesis That the Real Rate of Interest is Constant," American Economic Review, American Economic Association, vol. 67(3), pages 478-86, June.
  10. Gil-Alana, Luis A., 2000. "Mean reversion in the real exchange rates," Economics Letters, Elsevier, vol. 69(3), pages 285-288, December.
  11. Gil-Alana, L. A. & Robinson, P. M., 1997. "Testing of unit root and other nonstationary hypotheses in macroeconomic time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 241-268, October.
  12. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
  13. L. A. Gil-Alaña & Peter M. Robinson, 2001. "Testing of seasonal fractional integration in UK and Japanese consumption and income," LSE Research Online Documents on Economics 298, London School of Economics and Political Science, LSE Library.
  14. Laurence Ball & Stephen G. Cecchetti, 1990. "Inflation and Uncertainty at Long and Short Horizons," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 21(1), pages 215-254.
  15. Brunner, Allan D & Hess, Gregory D, 1993. "Are Higher Levels of Inflation Less Predictable? A State-Dependent Conditional Heteroscedasticity Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 187-97, April.
  16. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
  17. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
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  19. Gil-Alana, Luis A., 1999. "Testing fractional integration with monthly data," Economic Modelling, Elsevier, vol. 16(4), pages 613-629, December.
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