model averaging

Model averaging estimates the distribution of quantities of interest across models. Model averaging can be used for inference, prediction and policy analysis to address model uncertainty. Three main approaches are discussed: Bayesian model averaging (BMA), empirical Bayes (EB) methods, and frequentist model averaging (FMA). Differences in prior specifications are contrasted using the example of normal, linear regression models. Finally, the article discusses implementation issues such as numerical simulation techniques and software for model averaging.