Long memory time series and short term forecasts
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Bibliographic Info
Article provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 19 (2003)
Issue (Month): 3 ()
Pages: 477-491
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Web page: http://www.elsevier.com/locate/ijforecast
Related research
Keywords:References
References listed on IDEASPlease report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Andersson, Michael K., 1998. "Do Long-Memory Models Have Long Memory?," Working Paper Series in Economics and Finance 227, Stockholm School of Economics, revised 16 Mar 2000.
- Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
- Andersson, Michael K., 2000. "Do long-memory models have long memory?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 121-124.
- 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.
- 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.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- 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.
- Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro de, 2003.
"Convex Combinations of Long Memory Estimates from Different Sampling Rates,"
Economics Working Papers (Ensaios Economicos da EPGE)
489, Graduate School of Economics, Getulio Vargas Foundation (Brazil).
- 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.
- Heinen, Florian & Sibbertsen, Philipp & Kruse, Robinson, 2009.
"Forecasting long memory time series under a break in persistence,"
Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover
dp-433, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- Florian Heinen & Philipp Sibbertsen & Robinson Kruse, 2009. "Forecasting long memory time series under a break in persistence," CREATES Research Papers 2009-53, School of Economics and Management, University of Aarhus.
- Taisei Kaizoji & Thomas Lux, 2006.
"Forecasting Volatility and Volume in the Tokyo Stock Market: Long Memory, Fractality and Regime Switching,"
Working Papers
wp06-20, Warwick Business School, Financial Econometrics Research Centre.
- 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.
- Lux, Thomas & Kaizoji, Taisei, 2006. "Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching," Economics Working Papers 2006,13, Christian-Albrechts-University of Kiel, Department of Economics.
- 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,"
CFS Working Paper Series
2003/35, Center for Financial Studies.
- 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.
- 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.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2005. "Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," NBER Working Papers 11775, National Bureau of Economic Research, Inc.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," CREATES Research Papers 2007-18, School of Economics and Management, University of Aarhus.
- 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.
- 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.
- 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.
- John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Working Papers 07-1, Bank of Canada.
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