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Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors


  • Jan Beran


  • Yuanhua Feng



No abstract is available for this item.

Suggested Citation

  • Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
  • Handle: RePEc:spr:aistmt:v:54:y:2002:i:2:p:291-311 DOI: 10.1023/A:1022469818068

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    References listed on IDEAS

    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. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    3. Heiler, Siegfried & Feng, Yuanhua, 1995. "A simple root n bandwidth selector for nonparametric regression," Discussion Papers, Series II 286, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
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    Cited by:

    1. Beran, Jan & Feng, Yuanhua & Ghosh, Sucharita & Sibbertsen, Philipp, 2002. "On robust local polynomial estimation with long-memory errors," International Journal of Forecasting, Elsevier, vol. 18(2), pages 227-241.
    2. Klaus Abberger, 2004. "Nonparametric Regression and the Detection of Turning Points in the Ifo Business Climate," CESifo Working Paper Series 1283, CESifo Group Munich.
    3. Feng, Yuanhua & Zhou, Chen, 2015. "Forecasting financial market activity using a semiparametric fractionally integrated Log-ACD," International Journal of Forecasting, Elsevier, vol. 31(2), pages 349-363.
    4. Gao, Jiti & Robinson, Peter M., 2014. "Inference on nonstationary time series with moving mean," LSE Research Online Documents on Economics 66509, London School of Economics and Political Science, LSE Library.
    5. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    6. Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
    7. Beran, Jan & Weiershäuser, Arno, 2011. "On spline regression under Gaussian subordination with long memory," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 315-335, February.
    8. Yuanhua Feng, 2013. "An iterative plug-in algorithm for decomposing seasonal time series using the Berlin Method," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 266-281, February.
    9. Jing Wang, 2012. "Modelling time trend via spline confidence band," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 275-301, April.
    10. Boubaker, Heni & Sghaier, Nadia, 2015. "Semiparametric generalized long-memory modeling of some mena stock market returns: A wavelet approach," Economic Modelling, Elsevier, vol. 50(C), pages 254-265.
    11. Gao, Jiti & Robinson, Peter M., 2016. "Inference On Nonstationary Time Series With Moving Mean," Econometric Theory, Cambridge University Press, vol. 32(02), pages 431-457, April.
    12. Zhibiao Zhao & Yiyun Zhang & Runze Li, 2014. "Non-Parametric Estimation Under Strong Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 4-15, January.


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