Diagnostic Analytics for an Autoregressive Model under the Skew-Normal Distribution
Yonghui Liu, Guohua Mao, Víctor Leiva, Shuangzhe Liu, Alejandra Tapia
Abstract
Autoregressive models have played an important role in time series. In this paper, an autoregressive model based on the skew-normal distribution is considered. The estimation of its parameters is carried out by using the expectation–maximization algorithm, whereas the diagnostic analytics are conducted by means of the local influence method. Normal curvatures for the model under four perturbation schemes are established. Simulation studies are conducted to evaluate the performance of the proposed procedure. In addition, an empirical example involving weekly financial return data are analyzed using the procedure with the proposed diagnostic analytics, which has improved the model fit.