Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts

In this paper, we reassess the forecasting performance of the Bayesian mixed-frequency model suggested in Carriero et al. (2015) in terms of point and density forecasts of the GDP growth rate using US macroeconomic data. Following Chauvet and Potter (2013), we evaluate the forecasting accuracy of the model relative to a univariate AR(2) model separately for expansions and recessions, as defined by the NBER business cycle chronology, rather than relying on a comparison of forecast accuracy over the whole forecast sample spanning 1985Q1–2011Q3. We find that most of the evidence favouring the more sophisticated model over the simple benchmark model is due to relatively few observations during recessions, especially those during the Great Recession. In contrast, during expansions, the gains in forecasting accuracy over the benchmark model are at best very modest. This implies that the relative forecasting performance of the models varies with business cycle phases. Ignoring this fact results in a distorted picture, the relative performance of the more sophisticated model in comparison with the naive benchmark model tends to be overstated during expansions and understated during recessions.