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The Identification Of Seasonal Autoregressive Models

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  • Sergio G. Koreisha
  • Tarmo Pukkila

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.

Suggested Citation

  • Sergio G. Koreisha & Tarmo Pukkila, 1995. "The Identification Of Seasonal Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(3), pages 267-290, May.
    • Handle: RePEc:bla:jtsera:v:16:y:1995:i:3:p:267-290
    DOI: 10.1111/j.1467-9892.1995.tb00234.x
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    References listed on IDEAS

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    1. Koreisha, Sergio G & Pukkila, Tarmo, 1995. "A Comparison between Different Order-Determination Criteria for Identification of ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 127-131, January.
    2. Pukkila, Tarmo M., 1988. "An improved estimation method for univariate autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 27(2), pages 422-433, November.
    3. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
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    1. Koreisha, Sergio G. & Pukkila, Tarmo, 1998. "A two-step approach for identifying seasonal autoregressive time series forecasting models," International Journal of Forecasting, Elsevier, vol. 14(4), pages 483-496, December.

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