In this paper, we propose a new family of multivariate loss functions to test the rationality of vector forecasts without assuming independence across variables. When only one variable is of interest, the loss function reduces to the flexible asymmetric family proposed by Elliott, Komunjer, and Timmerman (2008). Following their methodology, we derive~a GMM test for multivariate forecast rationality that allows the forecaster's loss to be nonseparable across variables and takes into account forecast estimation uncertainty. We use our test to study the joint rationality of macroeconomic forecasts in the growth rate of nominal output, CPI inflation rate, and short-term interest rate.

This paper develops a framework for inferring common Markov-switching components in panel data sets with large cross-section and time series dimensions. We study similarities and differences across U.S. states in the timing of business cycles. We hypothesize that there exists a small number of cluster designations, with individual states in a given cluster sharing certain business cycle characteristics. We find that although oil-producing and agricultural states can sometimes experience a separate recession from the rest of the United States, for the most part, differences across states appear to be a matter of timing, with some states entering recession or recovering before others.