Macroeconomic forecasting is a very difficult task due to the lack of an accurate, convincing model of the economy. The most accurate models for economic forecasting, “black box” time series models, assume little about the structure of the economy. Constructing reliable time series models is challenging due to short data series, high noise levels, nonstationarities, and nonlinear effects. This chapter describes these challenges and presents some neural network solutions to them. Important issues include balancing the bias/variance tradeoff and the noise/nonstationarity tradeoff. A brief survey of methods includes hyperparameter selection (regularization parameter and training window length), input variable selection and pruning, network architecture selection and pruning, new smoothing regularizers, committee forecasts and model visualization. Separate sections present more in-depth descriptions of smoothing regularizers, architecture selection via the generalized prediction error (GPE) and nonlinear cross-validation (NCV), input selection via sensitivity based pruning (SBP), and model interpretation and visualization. Throughout, empirical results are presented for forecasting the U.S. Index of Industrial Production. These demonstrate that, relative to conventional linear time series and regression methods, superior performance can be obtained using state-of-the-art neural network models.