A0710
Title: Tensor PCA for factor models
Authors: Andrii Babii - University of North Carolina (United States) [presenting]
Eric Ghysels - University of North Carolina Chapel Hill (United States)
Junsu Pan - University of North Carolina Chapel Hill (United States)
Abstract: Modern empirical analysis often relies on high-dimensional panel datasets with non-negligible cross-sectional and time-series correlations. Factor models are natural for capturing such dependencies. A tensor factor model describes the multidimensional panel as a sum of a low-rank component and idiosyncratic noise, generalizing traditional factor models for two-dimensional panels. Several algorithms are considered to estimate the factors and factor loadings for tensor factor models. The asymptotic distribution theory is provided, and a test for the number of factors is proposed in a tensor factor model. The asymptotic results are supported by the Monte Carlo experiments, and the new tools are applied to sorted portfolios.
