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A Portmanteau Test for Smooth Transition Autoregressive Models

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  • Qiang Xia
  • Zhiqiang Zhang
  • Wai Keung Li

Abstract

This article investigates a portmanteau test statistic for checking model adequacy of smooth transition autoregressive (STAR) models. The asymptotic distribution of residual autocorrelations and the least‐squares estimators are also derived. Hence, the correct asymptotic standard errors for residual autocorrelations are also obtained facilitating model diagnostic checking. Through the graphical display of the simulation results concerning the size and power, for commonly used nominal sizes (≤0.1), the portmanteau test appears to be more advantageous than the Lagrange multiplier tests in checking serial independence for the errors of STAR models.

Suggested Citation

  • Qiang Xia & Zhiqiang Zhang & Wai Keung Li, 2020. "A Portmanteau Test for Smooth Transition Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 722-730, September.
    • Handle: RePEc:bla:jtsera:v:41:y:2020:i:5:p:722-730
    DOI: 10.1111/jtsa.12512
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    1. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    2. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    3. Lafaye de Micheaux, Pierre & Tran, Viet Anh, 2016. "PoweR: A Reproducible Research Tool to Ease Monte Carlo Power Simulation Studies for Goodness-of-fit Tests in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i03).
    4. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    5. Guodong Li & Wai Keung Li, 2005. "Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach," Biometrika, Biometrika Trust, vol. 92(3), pages 691-701, September.
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