Analysis, model selection and forecasting in univariate time series models can be routinely
carried out for models in which the model order is relatively small. Under an ARMA
assumption, classical estimation, model selection and forecasting can be routinely
implemented with the Box–Jenkins time domain representation. However, this approach
becomes at best prohibitive and at worst impossible when the model order is high. In
particular, the standard assumption of stationarity imposes constraints on the parameter …