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A Study of Autoregressive and Window Spectral Estimation

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  • N. Beamish
  • M. B. Priestley

Abstract

This paper describes the results of a simulation study aimed at comparing the relative merits of ar (autoregressive) and “Window” spectral estimation for stationary time series. Seven models are considered, namely ar(2), ar(4), ar(5), arma(2,2), ma(1) (two cases) and a process with a “mixed” spectrum. The paper also includes some discussion of two different methods of estimating the coefficients of ar models (the Burg method and the Yule–Walker approach), and of the performance of various order determination criteria, such as FPE, AIC and CAT.

Suggested Citation

  • N. Beamish & M. B. Priestley, 1981. "A Study of Autoregressive and Window Spectral Estimation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(1), pages 41-58, March.
    • Handle: RePEc:bla:jorssc:v:30:y:1981:i:1:p:41-58
    DOI: 10.2307/2346656
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    Cited by:

    1. Ionel Birgean & Lutz Kilian, 2002. "Data-Driven Nonparametric Spectral Density Estimators For Economic Time Series: A Monte Carlo Study," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 449-476.
    2. Charles Kooperberg & Charles J. Stone & Young K. Truong, 1995. "Logspline Estimation Of A Possibly Mixed Spectral Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(4), pages 359-388, July.
    3. Datta Gupta, Syamantak & Mazumdar, Ravi R. & Glynn, Peter, 2013. "On the convergence of the spectrum of finite order approximations of stationary time series," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 1-21.

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