Model selection for time series data based upon joint multiperiod forecasts is investigated. Some popular selection criteria are applied to a suitable multivariate regression model to create new selection criteria. Monte Carlo experiments show that the proposed selection procedure performs more efficiently than the corresponding regular procedure, particularly when the data are generated from a high order autoregressive model. Analysis of some real data supports the applicability of the proposed procedure, especially when the degree of differencing required is uncertain.
