This paper proposes a new method for combining forecasts based on complete subset regressions. For a given set of potential predictor variables we combine forecasts from all possible linear regression models that keep the number of predictors fixed. We explore how the choice of model complexity,...

In situations where a sequence of forecasts is observed, a common strategy is to examine "rationality" conditional on a given loss function. We examine this from a different perspective—supposing that we have a family of loss functions indexed by unknown shape parameters, then given the...

Empirical studies using survey data on expectations have frequently observed that forecasts are biased and have concluded that agents are not rational. We establish that existing rationality tests are not robust to even small deviations from symmetric loss and hence have little ability to tell...

Existing results on the properties and performance of forecast combinations have been derived in the context of mean squared error loss. Under this loss function empirical studies have generally found that estimates of optimal forecast combination weights lead to higher losses than...

We analyze the complete subset regression (CSR) approach of Elliott et al. (2013) in situations with many possible predictor variables. The CSR approach has the computational advantage that it can be applied even when the number of predictors exceeds the sample size. Theoretical results...

In situations where a sequence of forecasts is observed, a common strategy is to examine “rationality” conditional on a given loss function. We examine this from a different perspective— supposing that we have a family of loss functions indexed by unknown shape parameters, then given the...

Forecasts guide decisions in all areas of economics and finance and their value can only be understood in relation to, and in the context of, such decisions. We discuss the central role of the loss function in helping determine the forecaster's objectives. Decision theory provides a framework...

Empirical studies using survey data on expectations have frequently observed that forecasts are biased and have concluded that agents are not rational. We establish that existing rationality tests are not robust to even small deviations from symmetric loss and hence have little ability to tell...

Empirical studies using survey data on expectations have frequently observed that forecasts are biased and have concluded that agents are not rational. We establish that existing rationality tests are not robust to even small deviations from symmetric loss and hence have little ability to tell...