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Long-memory forecasting of US monetary indices

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  • Christopher F. Baum

    (Boston College, Chestnut Hill, Massachusetts, USA)

  • John Barkoulas

    (Georgia Southern University, Statesboro, Georgia, USA)

Abstract

Several studies have tested for long-range dependence in macroeconomic and financial time series but very few have assessed the usefulness of long-memory models as forecast-generating mechanisms. This study tests for fractional differencing in the US monetary indices (simple sum and divisia) and compares the out-of-sample fractional forecasts to benchmark forecasts. The long-memory parameter is estimated using Robinson's Gaussian semi-parametric and multivariate log-periodogram methods. The evidence amply suggests that the monetary series possess a fractional order between one and two. Fractional out-of-sample forecasts are consistently more accurate (with the exception of the M3 series) than benchmark autoregressive forecasts but the forecasting gains are not generally statistically significant. In terms of forecast encompassing, the fractional model encompasses the autoregressive model for the divisia series but neither model encompasses the other for the simple sum series.  Copyright © 2006 John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 25 (2006)
Issue (Month): 4 ()
Pages: 291-302

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Handle: RePEc:jof:jforec:v:25:y:2006:i:4:p:291-302

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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References

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  1. John Barkoulas & Christopher F. Baum & Mustafa Caglayan, 1998. "Fractional Monetary Dynamics," Boston College Working Papers in Economics 321., Boston College Department of Economics.
  2. Barkoulas, John T & Baum, Christopher F, 1997. "Fractional Differencing Modeling and Forecasting of Eurocurrency Deposit Rates," Journal of Financial Research, Southern Finance Association & Southwestern Finance Association, vol. 20(3), pages 355-72, Fall.
  3. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Is consumption too smooth? Long memory and the Deaton paradox," Finance and Economics Discussion Series 57, Board of Governors of the Federal Reserve System (U.S.).
  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. Franses, Philip Hans & Ooms, Marius, 1997. "A periodic long-memory model for quarterly UK inflation," International Journal of Forecasting, Elsevier, vol. 13(1), pages 117-126, March.
  6. Francis X. Diebold & Glenn D. Rudebusch, 1988. "Long memory and persistence in aggregate output," Finance and Economics Discussion Series 7, Board of Governors of the Federal Reserve System (U.S.).
  7. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.
  8. Diebold, Francis X. & Lindner, Peter, 1996. "Fractional integration and interval prediction," Economics Letters, Elsevier, vol. 50(3), pages 305-313, March.
  9. Cheung, Yin-Wong, 1993. "Long Memory in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 93-101, January.
  10. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  11. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  12. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
  13. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
  14. Christopher F. Baum & John T. Barkoulas & Mustafa Caglayan, 1999. "Persistence in International Inflation Rates," Southern Economic Journal, Southern Economic Association, vol. 65(4), pages 900-913, April.
  15. Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
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Cited by:
  1. Carlos P. Barros & Luis A. Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 2012/103, CEsA Centre of African and Development Studies.
  2. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2012. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," AMSE Working Papers 1214, Aix-Marseille School of Economics, Marseille, France.
  3. S. D. Grose & D. S. Poskitt, 2006. "The Finite-Sample Properties of Autoregressive Approximations of Fractionally-Integrated and Non-Invertible Processes," Monash Econometrics and Business Statistics Working Papers 15/06, Monash University, Department of Econometrics and Business Statistics.
  4. Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.
  5. Mohamed Chikhi & Anne Peguin-Feissolle & Michel Terraza, 2012. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Working Papers halshs-00793203, HAL.
  6. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Multi-Factor Gegenbauer Processes and European Inflation Rates," CESifo Working Paper Series 2648, CESifo Group Munich.

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