Economic Forecasting

Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns.Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters.This graduate textbook approaches forecasting problems from a decision theoretic and estimation perspective, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows readers how to generate forecasts in the presence of large-dimensional sets of predictor variables. The book pays special attention to the effects on forecasting performance of estimation error, model uncertainty, and model instability.Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methodsApproaches forecasting from a decision theoretic and estimation perspectiveCovers Bayesian modeling, including methods for generating density forecastsDiscusses model selection methods as well as forecast combinationsCovers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatilityFeatures numerous empirical examplesExamines the latest advances in forecast evaluationEssential for practitioners and students alike