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On The Robustness To Small Trends Of Estimation Based On The Smoothed Periodogram

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  • C. C. Heyde
  • W. Dai

Abstract

. In this paper we are concerned with the robustness of inferences, carried out on a stationary process contaminated by a small trend, to this departure from stationarity. It is shown that a smoothed periodogram approach to model fining and parameter estimation is highly robust to the presence of a small trend if the underlying stationary process is short‐range dependent. If the underlying process is long‐range dependent the robustness properties are still good but now depend on the Hurst index of the process and deteriorate with increasing Hurst index.

Suggested Citation

  • C. C. Heyde & W. Dai, 1996. "On The Robustness To Small Trends Of Estimation Based On The Smoothed Periodogram," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(2), pages 141-150, March.
    • Handle: RePEc:bla:jtsera:v:17:y:1996:i:2:p:141-150
    DOI: 10.1111/j.1467-9892.1996.tb00269.x
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    Cited by:

    1. Sibbertsen, Philipp, 2003. "Log-periodogram estimation of the memory parameter of a long-memory process under trend," Statistics & Probability Letters, Elsevier, vol. 61(3), pages 261-268, February.
    2. Javier Hualde & Morten Ørregaard Nielsen, 2022. "Truncated sum-of-squares estimation of fractional time series models with generalized power law trend," CREATES Research Papers 2022-07, Department of Economics and Business Economics, Aarhus University.
    3. Leipus, Remigijus & Viano, Marie-Claude, 2003. "Long memory and stochastic trend," Statistics & Probability Letters, Elsevier, vol. 61(2), pages 177-190, January.

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