An exponential-squared estimator in the autoregressive model with heavy-tailed errors

作者：Jiang, Yunlu^*

来源：Statistics and Its Interface, 2016, 9(2): 233-238.

DOI：10.4310/SII.2016.v9.n2.a10

• Autoregressive model
• Exponential squared loss
• Heavy-tailed distribution

In this paper, an exponential-squared estimator is introduced in the autoregressive model with heavy-tailed errors. Under some conditions, the root n-consistency of the proposed estimator is established. Since the exponential-squared estimator involves a tuning parameter A, we select A via a fivefold cross validation procedure. Simulation studies illustrate that the finite sample performance of proposed method performs better than that of a self-weighted composite quantile regression (SWCQR) method and self-weighted least absolute deviation (SWLAD) method in terms of Sd and MSE when the error follows a heavy-tailed distribution and there are outliers in the dataset. Finally, we apply the proposed methodology to analyze the Recruitment series.

• 出版日期2016
• 单位暨南大学