The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical techniques to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas including growth economics, production modeling, finance and forecasting macroeconomic quantities.

We consider a Bayesian implementation of a new approach to estimating Demand Systems. This approach, suggested by Varian (1990), is based on a generalization of Afriat's (1967) e-ciency index. The model we propose leads to a very tractable posterior and predictive analysis, yet allows for interesting economic interpretations. We conduct a sensitivity analysis with respect to the prior in an application to annual aggregate U.S. consumption data, and conclude that the sample is quite informative. Average e-ciency and expected budget shares are examined in some detail.