| Summary: | 碩士 === 國立臺北大學 === 統計學系 === 103 === In the modelling of non-Gaussian time series, one strategy is to retain the general autoregressive moving average (ARMA) framework and allow the white noise to be non-Gaussian distribution. In this work, we are interested in correlated data exhibiting asymmetry by adopting a non-Gaussian autoregressive model with Azzalini's (1985) skew-normal distribution and Bondon's (2009) epsilon-skew-normal innovations. The moments estimate, least squares estimate and conditional maximum likelihood estimates of the parameters are derived, and their limit distributions are proved. For small sample behavior, we assess the performance of proposed methods through Monte Carlo simulations. Finally, the flexibility of this model is illustrated by fitting it to a real time series.
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