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Abstract:
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We propose least tail-trimmed absolute deviation estimation for autoregressive processes with infinite/finite variance. We explore the large sample properties of the resulting estimator and establish its asymptotic normality. Moreover, we study convergence rates of the estimator under different moment settings, and show that it attains a super root-n convergence rate when the innovation variance is infinite. Relevant statistical inferences are conducted. Simulation studies are carried out to examine the finite-sample performance of the proposed method. An empirical example is also presented.
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