We will develop methods for forecasting important macroeconomic variables where a large set of predictors is available. As well as raw variables and composite indices such as principal components, we will also include various lags and nonlinear functions of potential predictors. We will adapt Bayesian statistical methods for selecting these predictors so that they can be applied to time series data, thus developing innovative forecasting methods that can be used on a range of important problems involving 'Big Data'. We will compare forecasts from different methods using simulated and empirical data from the US and Australia. For the latter we will compose an online handbook of available Australian economic data for public use.