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Prediction of 0-1-events for short- and long-memory time series

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  • Jan Beran

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    (Department of Mathematics and Statistics, University of Konstanz)

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    Abstract

    The problem of predicting 0-1-events is considered under general conditions, including stationary processes with short and long memory as well as processes with changing distribution patterns. Nonparametric estimates of the probability function and prediction intervals are obtained.

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    File URL: http://cofe.uni-konstanz.de/Papers/dp02_11.pdf
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    Bibliographic Info

    Paper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 02-11.

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    Length: 12 pages
    Date of creation: Mar 2002
    Date of revision:
    Handle: RePEc:knz:cofedp:0211

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    Related research

    Keywords: 0-1-events; long-range dependence; short-range dependence; antipersistence; kernel smoothing; bandwidth; prediction;

    This paper has been announced in the following NEP Reports:

    References

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    Please 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.:
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    1. Hall, Peter & Hart, Jeffrey D., 1990. "Nonparametric regression with long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 36(2), pages 339-351, December.
    2. Chiu, Shean-Tsong, 1989. "Bandwidth selection for kernel estimate with correlated noise," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 347-354, September.
    3. Beran, Jan & Feng, Yuanhua & Ocker, Dirk, 1999. "SEMIFAR models," Technical Reports 1999,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer, vol. 54(2), pages 291-311, June.
    5. Beran, Jan & Feng, Yuanhua, 2002. "SEMIFAR models--a semiparametric approach to modelling trends, long-range dependence and nonstationarity," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 393-419, August.
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