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It has been a standard assumption in theoretical macroeconomic modeling that agents are processing all the available quantities of information when forming their expectations for the future. Also, policymakers traditionally have looked at a vast array of indicator series in the run-up to major policy decisions, or in the words of Lars Svensson (Svensson (2005)) about what central bankers do in practice: ‘(l)arge amounts of data about the state of the economy and the rest of the world ... are collected, processed, and analyzed before each major decision.’ However, most traditional macroeconomic prediction approaches rarely consists of models that handle more than 10 variables, because it is either inefficient or downright impossible to incorporate a much larger number of variables in a single forecasting model and estimate it using standard econometric techniques. This failure of traditional macroeconomic forecasting methods prompted a new strand of research devoted to the theory and practice of alternative macroeconomic forecasting methods that utilize large data sets.

These alternative methods can be distinguished into two main categories. As, e.g., outlined in Hendry (1995), the methods of the first category involve inherently two steps: In the first step some form of variable selection is undertaken. The variables that are chosen are then used in a standard forecasting model. Recent developments in this line of research has focussed on automated model selection procedures in order to be better able to select the optimal predictors from large data sets; see Krolzig and Hendry (2001). An alternative group of forecasting methods consists of estimation strategies that allow estimation of a single equation model that utilizes all the information in a large data set and not just an ‘optimal’ subset of the available predictor series. This is a diverse group of forecasting methods ranging from factor-based methods to Bayesian regression and forecast combination. These two groups of methods inevitably overlap. However, we feel that the step of variable selection is, and involves methods that are, sufficiently distinct to merit separate mention and treatment. Instead, we focus in this paper on the latter group of data-rich forecasting methods.