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Nonparametric regression with long-memory errors

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  • Deo, R. S.

Abstract

The fixed design regression model with long-memory errors is considered. The finite-dimensional asymptotic distributions of the properly normalised kernel estimators of the regression function are shown to be normal when the errors are a linear process.

Suggested Citation

  • Deo, R. S., 1997. "Nonparametric regression with long-memory errors," Statistics & Probability Letters, Elsevier, vol. 33(1), pages 89-94, April.
  • Handle: RePEc:eee:stapro:v:33:y:1997:i:1:p:89-94
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    References listed on IDEAS

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    1. Csörgo, Sándor & Mielniczuk, Jan, 1995. "Distant long-range dependent sums and regression estimation," Stochastic Processes and their Applications, Elsevier, vol. 59(1), pages 143-155, September.
    2. 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.
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    Cited by:

    1. 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.
    2. Kris Brabanter & Farzad Sabzikar, 2021. "Asymptotic theory for regression models with fractional local to unity root errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(7), pages 997-1024, October.
    3. Robinson, Peter M., 1997. "Large-sample inference for nonparametric regression with dependent errors," LSE Research Online Documents on Economics 302, London School of Economics and Political Science, LSE Library.
    4. Gao, Jiti & Robinson, Peter M., 2016. "Inference On Nonstationary Time Series With Moving Mean," Econometric Theory, Cambridge University Press, vol. 32(2), pages 431-457, April.

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