A0458
Title: Testing for assessing vector correlation in high-dimensional settings
Authors: Takahiro Nishiyama - Senshu University (Japan) [presenting]
Masashi Hyodo - Kanagawa University (Japan)
Shoichi Narita - Kanagawa University (Japan)
Abstract: A new test for vector correlation is proposed in a high-dimensional framework while accommodating a low-dimensional latent factor model. The test, built under low-dimensional factor models, distinguished from previous normal approximation-based tests, which are valid under a weak spike structure. A modified RV coefficient for high-dimensional data is proposed, and it shows that its null-limiting distributions follow a weighted mixture of chi-square distributions under a high-dimensional asymptotic regime integrated with weak technical conditions. By applying this asymptotic result and estimation theory of the number of factors in a low-dimensional factor model, a new approximation test is proposed for vector correlations. Besides, the asymptotic power function is derived for the proposed test. The finite sample and dimensional performance of this test are also examined using Monte Carlo simulations.
