Time Series Model Specification in the Presence of Outliers

Abstract

Outliers are commonplace in data analysis. Time series analysis is no exception. Noting that the effect of outliers on model identification statistics could be serious, this article is concerned with the problem of time series model specification in the presence of outliers. An iterative procedure is proposed to identify the outliers, to remove their effects, and to specify a tentative model for the underlying process. The procedure is essentially based on the iterative estimation procedure of Chang and Tiao (1983) and the extended sample autocorrelation function (ESACF) model identification method of Tsay and Tiao (1984). An example is given. Properties of the proposed procedure are discussed.

Key Words:

• Autoregressive moving average model
• Extended sample autocorrelation function
• Model identification