Anderson, Oliver D.; De Gooijer, Jan G. Discrimination between nonstationary and nearly nonstationary processes, and its effect on forecasting. (English) Zbl 0699.62092 RAIRO, Rech. Opér. 24, No. 1, 67-91 (1990). Summary: We present theoretical and empirical evidence to show that the structure, for the observed serial dependence between the values of a series realisation, is quite sensitive to the distinction between a near- nonstationary model and a just nonstationary approximation to it. Reliable discrimination between the two may well be possible then, in practice, and this implies that improved modelling, as judged by increased forecasting effectiveness, can perhaps be achieved. We study exact and approximate measures of serial covariance and serial correlation, respectively, for a wide class of non-explosive linear time processes, including the ARMA and ARIMA models. Cited in 2 Documents MSC: 62M20 Inference from stochastic processes and prediction 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) Keywords:prediction; misspecified models; identifying time series; distributional properties; serial dependence; near-nonstationary model; nonstationary approximation; discrimination; forecasting effectiveness; measures of serial covariance; serial correlation; non-explosive linear time processes; ARMA; ARIMA models × Cite Format Result Cite Review PDF Full Text: DOI EuDML