The first AR(2) process, (ϕ1 = 0.5, ϕ2 = 0.45), is roughly like an AR(1) process with ϕ ≈ 0.95. This can explain why the results of the iGLS-AR(1) model are so close to the optimum estimated by the iGLS-AR(2). The second AR(2) process, (ϕ1 = 1.4, ϕ2 = −0.45), was selected to assess the misspecification error. Although the use of an AR(1) process in the GLS and iGLS models improves the LM model, these results are far from the best using an AR(2) specification. The autocorrelation function of the AR(2) process shows a periodicity pattern that cannot be approximated by an AR(1) process. Finally, the third AR(2), (ϕ1 = 1.5, ϕ2 = −0.75), shows the effect of the misspecification in a later horizon h = 5, making the results at that horizon for an AR(1) specification even worse than the LM model. Again, this is motivated by the periodicity pattern of the AR(2) due to the negative sign of ϕ2. In all cases, the specification iGLS–AR(p) is rather close to the optimum. However, the important advantage is that it avoids a closed specification form of the dependence structure. Finally, the GLS-AR(2) scenario was not considered in this table because the gls function of nlme package does not allow the estimation of any parameter of an AR(2) greater than 1 in absolute value. This is an empirical rule in the package that avoids the use of non stationary processes although, in this case, the three AR(2) specifications are clearly stationary, but only the first specification can be estimated using the gls function.