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Order Identification Statistics In Stationary Autoregressive Moving‐Average Models:Vector Autocorrelations And The Bootstrap

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  • Efstathios Paparoditis
  • Bernd Streitberg

Abstract

. In this paper we consider the vector autocorrelation approach for identifying ARMA (p, q) models and use a bootstrap procedure in order to evaluate the distribution of the corresponding sample statistics by means of a resampling scheme for the residuals when p and q are unknown. The asymptotic validity of the bootstrap procedure applied to the vector autocorrelation estimates is established. Some simulations and examples demonstrating the appropriateness of the proposed bootstrap procedure in comparison with large‐sample Gaussian approximations are included.

Suggested Citation

  • Efstathios Paparoditis & Bernd Streitberg, 1992. "Order Identification Statistics In Stationary Autoregressive Moving‐Average Models:Vector Autocorrelations And The Bootstrap," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(5), pages 415-434, September.
    • Handle: RePEc:bla:jtsera:v:13:y:1992:i:5:p:415-434
    DOI: 10.1111/j.1467-9892.1992.tb00117.x
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    Cited by:

    1. Alexander Braumann & Jens‐Peter Kreiss & Marco Meyer, 2021. "Simultaneous inference for autocovariances based on autoregressive sieve bootstrap," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 534-553, September.
    2. Hella, Heikki, 2003. "On robust ESACF identification of mixed ARIMA models," Bank of Finland Scientific Monographs, Bank of Finland, volume 0, number sm2003_027, March.

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