Quasi-likelihood estimation of structure-changed threshold double autoregressive models

Feifei Guo, Shiqing Ling*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

This paper investigates the quasi-maximum likelihood estimator (QMLE) of the structure-changed and two-regime threshold double autoregressive model. It is shown that both the estimated threshold and change-point are n-consistent, and they converge weakly to the smallest minimizer of a compound Poisson process and the location of minima of a two-sided random walk, respectively. Other estimated parameters are n−consistent and asymptotically normal. The performance of the QMLE is assessed via simulation studies and a real example is given to illustrate our procedure.

源语言英语
页(从-至)138-155
页数18
期刊Journal of Statistical Planning and Inference
205
DOI
出版状态已出版 - 3月 2020
已对外发布

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Guo, F., & Ling, S. (2020). Quasi-likelihood estimation of structure-changed threshold double autoregressive models. Journal of Statistical Planning and Inference, 205, 138-155. https://doi.org/10.1016/j.jspi.2019.06.008