Maximum Likelihood Estimation in Threshold Autoregression

The study focuses on robust estimation of the parameters of autoregressive threshold models carried out by means of the maximum likelihood estimation method with an optionally convex objective. We proved the asymptotic normalcy of these estimates and studied their asymptotic relative efficiency with respect to least squares and least absolute deviation estimates and to the maximum likelihood estimation with convex objective

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