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Diagnostic checking for non-stationary ARMA models with an application to financial data

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  • Ling, Shiqing
  • Zhu, Ke
  • Yee, Chong Ching

Abstract

This paper first derives the limiting distributions of the residual and the squared residual autocorrelation functions of the nonstationary autoregressive moving-average model, respectively. We then use them to construct two portmanteau statistics for testing the adequacy of the fitted model. Simulation results show that the tests have reasonable empirical sizes and powers in the finite samples. Finally, we use the daily SP500 data to illustrate our theory and approach.

Suggested Citation

  • Ling, Shiqing & Zhu, Ke & Yee, Chong Ching, 2013. "Diagnostic checking for non-stationary ARMA models with an application to financial data," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 624-639.
    • Handle: RePEc:eee:ecofin:v:26:y:2013:i:c:p:624-639
    DOI: 10.1016/j.najef.2013.02.025
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    References listed on IDEAS

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    1. Dong Wan Shin & Jong Hyup Lee, 1996. "Distribution Of Residual Autocorrelations In Nonstationary Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(1), pages 105-109, January.
    2. Sook Fwe Yap & Gregory C. Reinsel, 1995. "Results On Estimation And Testing For A Unit Root In The Nonstationary Autoregressive Moving‐Average Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(3), pages 339-353, May.
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    Cited by:

    1. Chia-Lin Chang & Allen, David & McAleer, Michael, 2013. "Recent developments in financial economics and econometrics: An overview," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 217-226.

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