This paper considers model selection and model averaging in panel data models with a multifactor error structure. We investigate the limiting distributions of the common correlated effects estimators (Pesaran, 2006) in a local asymptotic framework and show that the trade-off between bias and variance remains in the asymptotic theory. In addition, we find that adding more regressors could have positive or negative effects on estimation variance. We then propose a focused information criterion and a plug-in averaging estimator for large heterogeneous panels. The novel feature of the proposed method is that it aims to minimize the sample analog of the asymptotic mean squared error and can apply to the cases irrespective of whether the rank condition holds or not. Monte Carlo simulations show that both proposed selection and averaging methods generally achieve lower expected squared error than other methods.