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Hidden Frequency Estimation with Data Tapers

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  • Zhao‐Guo Chen
  • Ka Ho Wu
  • Rainer Dahlhaus

Abstract

The detection and estimation of hidden frequencies has long been recognized as an important problem in time series. In this paper we study the asymptotic theory for two methods of high‐precision estimation of hidden frequencies (the secondary analysis method and the maximum periodogram method) using a data taper. In ordinary situations, a data taper may reduce the estimation precision slightly. However, when there are high peaks in the spectral density of the noise or other strong hidden periodicities with frequencies close to the hidden frequency of interest, the procedures for detection of the existence of and estimation of the hidden frequency of interest fail if data are nontapered whereas they may work well if the data are tapered. The theoretical results are verified by some simulated examples.

Suggested Citation

  • Zhao‐Guo Chen & Ka Ho Wu & Rainer Dahlhaus, 2000. "Hidden Frequency Estimation with Data Tapers," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(2), pages 113-142, March.
    • Handle: RePEc:bla:jtsera:v:21:y:2000:i:2:p:113-142
    DOI: 10.1111/1467-9892.00177
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    Cited by:

    1. McCoy, E. J. & Stephens, D. A., 2004. "Bayesian time series analysis of periodic behaviour and spectral structure," International Journal of Forecasting, Elsevier, vol. 20(4), pages 713-730.
    2. Chen, Bei & Gel, Yulia R., 2010. "Autoregressive frequency detection using Regularized Least Squares," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1712-1727, August.

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