Inference in Group Factor Models With an Application to Mixed‐Frequency Data

By E. Andreou, Patrick Gagliardini, Eric Ghysels, M. Rubin. Econometrica, 2019

53 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/a8f95e94845c60660caa1806444248bdf8c5d170

Citations in the corpus

• State-Varying Factor Models of Large Dimensions (2018, unknown): “2 The theoretical framework for the factor estimation is closely related to Bai (2003)’s and Su and Wang (2017)’s inferential theory of the PCA estimator. While Bai (2003) applies PCA to the unconditional covariance matrix, Su and Wang (2017) use the spot covariance matrix conditioned on a particula”