Real-Time Nowcasting Nominal GDP Under Structural Break

This paper provides early assessments of current U.S. Nominal GDP growth, which has been con- sidered as a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy makers have available at the time predictions are made. However, real time information arrives at di¤erent frequencies and asynchronously, which poses the challenge of mixed frequencies, missing data, and ragged edges. This paper proposes a multivariate state space model that not only takes into account asynchronous information in?ow it also allows for potential parame- ter instability. We use small scale con?rmatory factor analysis in which the candidate variables are selected based on their ability to forecast GDP nominal. The model is fully estimated in one step using a nonlinear Kalman ?lter, which is applied to obtain simultaneously both optimal inferences on the dynamic factor and parameters. Di¤erently from principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate speci?cations. The results indicate that the proposed model containing information on real economic activity, in?ation, interest rates, and Divisia monetary aggregates produces the most accurate real time nowcasts of nominal GDP growth.