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Long memory time series and short term forecasts

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  • Man, K. S.

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  • Man, K. S., 2003. "Long memory time series and short term forecasts," International Journal of Forecasting, Elsevier, vol. 19(3), pages 477-491.
    • Handle: RePEc:eee:intfor:v:19:y:2003:i:3:p:477-491
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    1. 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.
    2. Andersson, Michael K., 1998. "Do Long-Memory Models Have Long Memory?," SSE/EFI Working Paper Series in Economics and Finance 227, Stockholm School of Economics, revised 16 Mar 2000.
    3. Bhansali, R. J. & Kokoszka, P. S., 2002. "Computation of the forecast coefficients for multistep prediction of long-range dependent time series," International Journal of Forecasting, Elsevier, vol. 18(2), pages 181-206.
    4. Hurvich, Clifford M., 2002. "Multistep forecasting of long memory series using fractional exponential models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 167-179.
    5. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    6. Andersson, Michael K., 2000. "Do long-memory models have long memory?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 121-124.
    7. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    8. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    1. Reisen, Valdério A. & Zamprogno, Bartolomeu & Palma, Wilfredo & Arteche, Josu, 2014. "A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 1-17.
    2. Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
    3. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Relative forecasting performance of volatility models: Monte Carlo evidence," Kiel Working Papers 1582, Kiel Institute for the World Economy (IfW Kiel).
    4. Assaf, A., 2006. "Dependence and mean reversion in stock prices: The case of the MENA region," Research in International Business and Finance, Elsevier, vol. 20(3), pages 286-304, September.
    5. Man Kasing, 2010. "Extended Fractional Gaussian Noise and Simple ARFIMA Approximations," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-26, September.
    6. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    7. Lux, Thomas & Kaizoji, Taisei, 2004. "Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models," Economics Working Papers 2004-05, Christian-Albrechts-University of Kiel, Department of Economics.
    8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," PIER Working Paper Archive 03-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Sep 2003.
    9. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    10. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    11. Heinen, Florian & Sibbertsen, Philipp & Kruse, Robinson, 2009. "Forecasting long memory time series under a break in persistence," Hannover Economic Papers (HEP) dp-433, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    12. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    13. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    14. Man, K.S. & Tiao, G.C., 2006. "Aggregation effect and forecasting temporal aggregates of long memory processes," International Journal of Forecasting, Elsevier, vol. 22(2), pages 267-281.
    15. Nidhi Choudhary & Girish K. Nair & Harsh Purohit, 2015. "Volatility In Copper Prices In India," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-26, December.
    16. Leonardo Souza & Jeremy Smith & Reinaldo Souza, 2006. "Convex combinations of long memory estimates from different sampling rates," Computational Statistics, Springer, vol. 21(3), pages 399-413, December.
    17. J. Eduardo Vera-Vald'es, 2018. "Nonfractional Memory: Filtering, Antipersistence, and Forecasting," Papers 1801.06677, arXiv.org.
    18. K. S. Man & G. C. Tiao, 2009. "ARFIMA approximation and forecasting of the limiting aggregate structure of long-memory process," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 89-101.
    19. Tao Yin & Yiming Wang, 2019. "Predicting the Price of WTI Crude Oil Using ANN and Chaos," Sustainability, MDPI, vol. 11(21), pages 1-14, October.
    20. Jinliang Li & Chunchi Wu, 2006. "Daily Return Volatility, Bid-Ask Spreads, and Information Flow: Analyzing the Information Content of Volume," The Journal of Business, University of Chicago Press, vol. 79(5), pages 2697-2740, September.

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