Improving forecasts using equally weighted predictors

• Published in: Journal of business research Vol. 68; no. 8; pp. 1792 - 1799
• Main Author: Graefe, Andreas
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.08.2015; Elsevier Sequoia S.A
• Subjects: Differential weights; Econometric models; Economic forecasts; Elections; Equal weights; Forecasting techniques; Index method; Mathematical models; Measurement errors; Political behaviour; Predictions; Presidential election forecasting; Regression; Regression analysis; Studies; U.S.A; Variables; United States > US; Equal weights; Index method; Regression; Econometric models; Presidential election forecasting; Differential weights
• ISSN: 0148-2963; 1873-7978
• DOI: 10.1016/j.jbusres.2015.03.038

The usual procedure for developing linear models to predict any kind of target variable is to identify a subset of most important predictors and to estimate weights that provide the best possible solution for a given sample. The resulting “optimally” weighted linear composite is then used when predicting new data. This approach is useful in situations with large and reliable datasets and few predictor variables. However, a large body of analytical and empirical evidence since the 1970s shows that such optimal variable weights are of little, if any, value in situations with small and noisy datasets and a large number of predictor variables. In such situations, which are common for social science problems, including all relevant variables is more important than their weighting. These findings have yet to impact many fields. This study uses data from nine U.S. election-forecasting models whose vote-share forecasts are regularly published in academic journals to demonstrate the value of (a) weighting all predictors equally and (b) including all relevant variables in the model. Across the ten elections from 1976 to 2012, equally weighted predictors yielded a lower forecast error than regression weights for six of the nine models. On average, the error of the equal-weights models was 5% lower than the error of the original regression models. An equal-weights model that uses all 27 variables that are included in the nine models missed the final vote-share results of the ten elections on average by only 1.3 percentage points. This error is 48% lower than the error of the typical, and 29% lower than the error of the most accurate, regression model.