Alhorn, KiraDette, HolgerSchorning, Kirsten2019-04-032019-04-032019http://hdl.handle.net/2003/3797910.17877/DE290R-19964In this paper we construct optimal designs for frequentist model averaging estimation. We derive the asymptotic distribution of the model averaging estimate with fixed weights in the case where the competing models are non-nested and none of these models is correctly specified. A Bayesian optimal design minimizes an expectation of the asymptotic mean squared error of the model averaging estimate calculated with respect to a suitable prior distribution. We demonstrate that Bayesian optimal designs can improve the accuracy of model averaging substantially. Moreover, the derived designs also improve the accuracy of estimation in a model selected by model selection and model averaging estimates with random weights.enDiscussion Paper / SFB823;7/2019model selectionBayesian optimal designsoptimal designmodel uncertaintymodel averaging310330620working paper