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ARMA MODELLING WITH NON‐GAUSSIAN INNOVATIONS
Author(s) -
Li W. K.,
McLeod A. I.
Publication year - 1988
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.1467-9892.1988.tb00461.x
Subject(s) - mathematics , asymptotic distribution , estimator , statistic , gaussian , normality , goodness of fit , residual , statistics , maximum likelihood , autoregressive–moving average model , series (stratigraphy) , econometrics , autoregressive model , algorithm , paleontology , physics , quantum mechanics , biology
. The problem of modelling time series driven by non‐Gaussian innovations is considered. The asymptotic normality of the maximum likelihood estimator is established under some general conditions. The distribution of the residual autocorrelations is also obtained. This gives rise to a potentially useful goodness‐of‐fit statistic. Applications of the results to two important cases are discussed. Two real examples are considered.