Do consumer survey data help improve US vehicle sales forecasts?

Abstract

PurposeThis study is concerned with evaluating the Federal Reserve forecasts of light motor vehicle sales. The goal is to assess accuracy gains from using consumer vehicle-buying attitudes and expectations about future business conditions derived from the long-running Michigan Surveys of Consumers.Design/methodology/approachSimplicity is a core principle in forecasting, and the literature provides plentiful evidence that combining forecasts from different methods and models reduces out-of-sample forecast errors if the methods and models are valid. As such, the authors construct a simple vector autoregressive (VAR) model that incorporates consumer vehicle-buying attitudes and expectations about future business conditions. Comparable forecasts of vehicle sales from this model are then combined with the Federal Reserve forecasts to assess accuracy gains.FindingsThe findings for 1994–2016 indicate that the Federal Reserve and VAR forecasts contain distinct and useful predictive information, and the combination of the two forecasts shows reductions in forecast errors that are more significant at longer horizons. The authors thus conclude that there are accuracy gains from using consumer survey responses.Originality/valueThis is the first study that is concerned with evaluating the Federal Reserve forecasts of vehicle sales and examines whether there are accuracy gains from using consumer vehicle-buying attitudes and expectations.