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Root-n-consistent estimation in partial linearmodels with long-memory errors

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1998

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Ghosh, Sucharita

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Scandinavian journal of statistics. 1998, 25(2), pp. 345-357. Available under: doi: 10.1111/1467-9469.00108

Zusammenfassung

We consider estimation of β in the semiparametric regression model y(i) - xT(i)β + f(i/n) + ε(i) where x(i) = g(i)/n) + e(i, f and g are unknown smooth functions and the processes ε(i) and e(i) are stationary with short- or long-range dependence. For the case of i.i.d. errors, Speckman (1988) proposed a √n–consistent estimator of β. In this paper it is shown that, under suitable regularity conditions, this estimator is asymptotically unbiased and √n–consistent even if the errors exhibit long-range dependence. The orders of the finite sample bias and of the required bandwidth depend on the long-memory parameters. Simulations and a data example illustrate the method

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510 Mathematik

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long memory, long-range dependence, partial linear model, semiparametric estimation, semiparametric regression

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ISO 690BERAN, Jan, Sucharita GHOSH, 1998. Root-n-consistent estimation in partial linearmodels with long-memory errors. In: Scandinavian journal of statistics. 1998, 25(2), pp. 345-357. Available under: doi: 10.1111/1467-9469.00108
BibTex
@article{Beran1998Rootn-18831,
  year={1998},
  doi={10.1111/1467-9469.00108},
  title={Root-n-consistent estimation in partial linearmodels with long-memory errors},
  number={2},
  volume={25},
  journal={Scandinavian journal of statistics},
  pages={345--357},
  author={Beran, Jan and Ghosh, Sucharita}
}
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