Forecasts of growth in US durables spending: assessing the usefulness of disaggregated consumer survey data

Abstract

This study aims to improve the accuracy of the Federal Reserve forecasts of growth in durables spending using disaggregated consumer survey data. Test results for 1988–2016 indicate that these forecasts do (do not) contain past information in consumer durables-buying (home-buying) attitudes of 35–54-year-old participants, participants with a college degree, male participants, and participants with the top 33% income. Using real-time data on durables spending and information in consumer home-buying attitudes and expectations, we construct a knowledge model (KM) to generate comparable forecasts of growth in durables spending. Our results indicate that the one- and four-quarter-ahead KM forecasts can potentially help improve the accuracy of Federal Reserve forecasts. Further results indicate that the one- and four-quarter-ahead combined Federal Reserve and KM forecasts show significant reductions in forecast errors, meaning that there are accuracy gains from using disaggregated consumer survey data. The practical implication is that forecasters should pay special attention to consumer home-buying attitudes and expectations about future business conditions, and policymakers should make use of such survey measures in monitoring the economy in real time.