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THE IDENTIFICATION OF SEASONAL AUTOREGRESSIVE MODELS
Author(s) -
Koreisha Sergio G.,
Pukkila Tarmo
Publication year - 1995
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1995.tb00234.x
Subject(s) - autoregressive model , star model , mathematics , white noise , identification (biology) , econometrics , nonlinear autoregressive exogenous model , autoregressive–moving average model , setar , autoregressive integrated moving average , statistics , time series , botany , biology
. 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.