Abstract:
One of the most powerful and widely used methodologies for forecasting economic time
series is the class of models known as seasonal autoregressive processes. In this paper
presents a new approach not only for identifying seasonal autoregressive models, but also
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 twostage
autoregressive order determination criterion. Once the order of the process is
determined the identified structure is tested to see if it can be simplified. The identification
performance of this procedure is contrasted with other order selection procedures for
models with 'gaps.' We also illustrate the forecast performance of the identification
method using yearly and quarterly economic data.
