A0614
Title: Self-weighted LAD-based inference for heavy-tailed continuous threshold autoregressive models
Authors: Yaxing Yang - Xiamen University (China) [presenting]
Abstract: The self-weighted least absolute deviation estimation (SLADE) of a heavy-tailed continuous threshold autoregressive (TAR) model is investigated. It is shown that the SLADE is strongly consistent and asymptotically normal. A sign-based portmanteau test is also developed for the diagnostics checking. Simulation studies are carried out to assess the finite-sample performance of our estimator and test. Finally, an empirical example is given to illustrate the usefulness of our method. Combined with a previous work, a complete asymptotic theory on the SLADE of a heavy-tailed TAR model is established. This enriches asymptotic theory on statistical inference for threshold models in the literature.
