The aim of this paper is to explain why cross-sectional estimated migration correlations displayed in the academic and professional literature can be either not consistent, or inefficient, and to discuss alternative approaches. The analysis relies on a model with stochastic migration in which the parameters of interest, that are migration correlations, are precisely defined. The impossibility of estimating consistently the migration correlations from cross-sectional data only is emphasized. We explain how to handle with individual rating histories, how to weight appropriately the cross-sectional estimators and how to estimate efficiently the joint migration probabilities at longer horizons.

We derive asymptotic properties of estimators and test statistics to determine—in a grouped data setting—common versus group‐specific factors. Despite the fact that our test statistic for the number of common factors, under the null, involves a parameter at the boundary (related to unit canonical correlations), we derive a parameter‐free asymptotic Gaussian distribution. We show how the group factor setting applies to mixed‐frequency data. As an empirical illustration, we address the question whether Industrial Production (IP) is still the dominant factor driving the U.S. economy using a mixed‐frequency data panel of IP and non‐IP sectors. We find that a single common factor explains 89% of IP output growth and 61% of total GDP growth despite the diminishing role of manufacturing.