Combining Forecasts: Multiple Regression versus a Bayesian Approach*

Simple linear combinations of forecasts have consistently been found to be more accurate than individual forecasts. Several recent studies have found that combination forecasts derived by constrained or unconstrained multiple regression are more accurate than a simple average of individual forecasts. This study uses macroeconomic data to compare the accuracy of combination forecasts derived by a Bayesian methodology with the accuracy of composite forecasts derived by multiple regression. Using the forecasts of four macroeconomic variables from five well-known econometric models, the study finds that the Bayesian combination procedure produces more accurate composite forecasts than does the regression combination procedure, based on a version of Theil's U² statistic.