Model averaging often improves forecast accuracy over individual forecasts. It may also be seen as a means of forecasting in data-rich environments. Bayesian model averaging methods have been widely advocated, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of informationtheoretic model averaging in forecasting UK in ation, with a large data set, and nd that it can be a powerful alternative to Bayesian averaging schemes.

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4Non-technical summary 5

Most macroeconomic data are uncertain -they are estimates rather than perfect measures of underlying economic variables. One symptom of that uncertainty is the propensity of statistical agencies to revise their estimates in the light of new information or methodological advances. This paper sets out an approach for extracting the signal from uncertain data. It describes a two-step estimation procedure in which the history of past revisions are first used to estimate the parameters of a measurement equation describing the official published estimates. These parameters are then imposed in a maximum likelihood estimation of a state space model for the macroeconomic variable.Keywords: Real-time data analysis; State space models; Data uncertainty; Data revisions JEL codes: C32, C53 * This paper represents the views and analysis of the authors and should not be thought to represent those of the Bank of England, Monetary Policy Committee, or any other organisation to which the authors are affiliated. We would like to thank the associate editor for extremely helpful comments on an early version of the paper. We have further benefited from helpful comments from Andrew Blake, Spencer Dale, Kevin Lee, Lavan Mahadeva, Tony Yates and Shaun Vahey. We would also like to thank Simon Van Norden for his constructive and incisive comments especially on the relation between our model and comparable recent models in the literature.

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