B1125
Title: Testing for common structures in high-dimensional factor models
Authors: Marie Duker - FAU Erlangen (Germany) [presenting]
Abstract: A novel testing procedure is discussed to explore common structures across two high-dimensional factor models. The introduced test allows for uncovering whether two-factor models are driven by the same loading matrix up to some linear transformation. The test can be used to discover inter-individual relationships between two data sets. In addition, it can be applied to test for changes in the loading matrix, effectively restricting the set of possible alternatives. The theoretical results cover the asymptotic behavior of the introduced test statistic. The theory is supported by a simulation study showing promising empirical test size and power results. A data application investigates the relationship between two macroeconomic indices collected for a large number of different industries.
