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Data-driven estimation of semiparametric fractional autoregressive models

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Author Info

  • Jan Beran

    ()
    (Center of Finance and Econometrics)

  • Yuanhua Feng

    ()
    (Center of Finance and Econometrics)

Abstract

In this paper data-driven algorithms for fitting SEMIFAR models (Beran, 1999) are proposed. The algorithms combine the data-driven estimation of the nonparametric trend and maximum likelihood estimation of the parameters. For selecting the bandwidth, the proposal of Beran and Feng (1999) based on the iterative plug-in idea (Gasser et al., 1991) is used. Asymptotic properties of the proposed algorithms are investigated. A large simulation study illustrates the practical performance of the methods.

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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 00-16.

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Length: 31 Pages
Date of creation: Jun 2000
Date of revision:
Handle: RePEc:knz:cofedp:0016

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Cited by:
  1. Jan Beran & Yuanhua Feng & Sucharita Gosh & Philipp Sibbertsen, 2000. "On robust local polynomial estimation with long-memory errors," CoFE Discussion Paper 00-18, Center of Finance and Econometrics, University of Konstanz.
  2. Jan Beran & Yuanhua Feng & Siegfried Heiler, 2000. "Modifying the double smoothing bandwidth selector in nonparametric regression," CoFE Discussion Paper 00-37, Center of Finance and Econometrics, University of Konstanz.
  3. 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.
  4. Jan Beran & Yuanhua.Feng, 2001. "Iterative plug-in algorithms for SEMIFAR models - definition, convergence and asymptotic properties," CoFE Discussion Paper 01-11, Center of Finance and Econometrics, University of Konstanz.

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