A1290
Title: Multilevel main effects matrix factor model
Authors: Clifford Lam - London School of Economics and Political Science (United Kingdom) [presenting]
Zetai Cen - University of Bristol (United Kingdom)
Kaixin Liu - London School of Economics and Political Science (United Kingdom)
Abstract: The aim is to propose a multilevel main effects matrix factor model (MMEFM) to extract meaningful row and column effects from groups of matrix data. As a generalization of a multilevel matrix factor model, rigorous definitions of MMEFM and identifications are provided, together with iterative algorithms for the estimators of all global and local components in the main effects and common component, including global/local main effects and core ranks, with rates of convergence spelt out. An important feature of the model is that all main effects can be nonstationary and still be consistently estimated at each time point. The objective is to propose a test for testing if a multilevel matrix factor model is sufficient against the alternative that MMEFM is necessary for the data. The power of the test, together with the accuracy of various estimators, is demonstrated in extensive simulation settings. Estimation performance is also compared to other state-of-the-art methods for matrix time series using simulations and a real data set.
