Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches

Multiple linear regression (MLR) is a popular method for producing forecasts when data on relevant independent variables (or cues) is available. The accuracy of the technique in forecasting the impact on Greek TV audience shares of programmes showing sport events is compared with forecasts produced by: (1) a simple bivariate regression model, (2) three different types of artificial neural network, (3) three forms of nearest neighbour analysis and (4) human judgment. MLR was found to perform relatively poorly. The application of Theil's bias decomposition and a Brunswik lens decomposition suggested that this was because of its inability to handle complex non-linearities in the relationship between the dependent variable and the cues and its tendency to overfit the in-sample data. Much higher accuracy was obtained from forecasts based on a simple bivariate regression model, a simple nearest neighbour procedure and from two of the types of artificial neural network. © 2006 Elsevier B.V. All rights reserved.