In this article, we introduce and study a one sided tempered stable autoregressive (TAR) process. Under the assumption of stationarity of the model, the marginal probbaility density function of the error term is found. It is shown that the distribution of error is infinitely divisible. Parameter estimation of the introduced TAR process is done by adopting the conditional least square and moments based approach and the performance of the proposed methods is shown on simulated data. Our model generalize the inverse Gaussian and one-sided stable autoregressive models.

翻译：在本篇文章中,我们引入并研究一个侧面的温和稳定的自动递减进程。在模型的固定性假设下,发现错误术语的边际粗密度函数。显示错误的分布是无限的,引入的TAR过程的参数估计是通过采用有条件的最小平方和瞬间方法进行的,而拟议方法的性能则在模拟数据中显示。我们的模型对反高斯和片面稳定的自动递减模型进行了概括。