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The limiting behavior of least absolute deviation estimators for threshold autoregressive models

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  • Wang, Lihong
  • Wang, Jinde

Abstract

The asymptotic behavior of the least squares (LS) estimators of the parameters in threshold autoregressive models has been completely studied in the literature. It is well known that in some cases the least absolute deviation (LAD) estimators are superior to the LS-estimators. This paper is devoted to studying the strong consistency and the asymptotic normality of the LAD-estimators in two cases where the threshold is known and/or unknown.

Suggested Citation

  • Wang, Lihong & Wang, Jinde, 2004. "The limiting behavior of least absolute deviation estimators for threshold autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 89(2), pages 243-260, May.
    • Handle: RePEc:eee:jmvana:v:89:y:2004:i:2:p:243-260
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    Cited by:

    1. Gabriela Ciuperca, 2011. "Penalized least absolute deviations estimation for nonlinear model with change-points," Statistical Papers, Springer, vol. 52(2), pages 371-390, May.

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