THE IDENTIFICATION OF SEASONAL AUTOREGRESSIVE MODELS

Sergio G. Koreisha and Tarmo Pukkila

Journal of Time Series Analysis, 1995, vol. 16, issue 3, 267-290

Abstract: Abstract. In this paper we present a new approach for identifying seasonal autoregressive models and the degree of differencing required to induce stationarity in the data. The identification method is iterative and consists in systematically fitting increasing order models to the data and then verifying that the resulting residuals behave like white noise using a two‐stage autoregressive order determination criterion. Once the order of the process is determined the identified structure is tested to see if it can be simplified. Simulation experiments based on different model structures with varying numbers of observations and parameter values as well as some macroeconomic data are used to evaluate the performance of the procedure.

Date: 1995
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