Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper examines the predictive content of coincident variables for monitoring US recessions in the presence of instabilities. We propose several specifications of the probit model for classifying phases of the business cycle. We find strong evidence in favor of those that allow for the possibility that the economy has experienced recurrent breaks. The recession probabilities of these models provide a clearer classification of the business cycle into expansion and recession periods, and superior performance in the ability to correctly call recessions and avoid false recession signals. Overall, the sensitivity, specificity, and accuracy of these models are far superior, as is their ability to signal recessions in a timely fashion. The results indicate the importance of considering recurrent breaks for monitoring business cycles.