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the parameters in g Varvg, where vg is the T 1 error vector for each g, is difficult when group effects have been removed. Estimates based on the FE residuals, v̂gt, disappear as T , but can be substantial. In AR(1) case, ̂ comes from

v̂gt on v̂g,t1, t 2, . . . , T, g 1, . . . , G.

One way to account for bias in ̂: use fully robust inference. But, as Hansen (2007b) shows, this can be very inefficient relative to his suggestion to bias-adjust the estimator ̂ and then use the bias-adjusted estimator in feasible GLS. (Hansen covers the general ARpmodel.) Hansen shows that an iterative bias-adjusted procedure has the same asymptotic distribution as ̂ in the case ̂ should work well: G and T both tending to infinity. Most importantly for the

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