City Research Online

Abstract

In this paper, we develop a two-step maximum likelihood estimator of time-varying loadings in high-dimensional factor models. We specify the loadings to evolve as stationary vector autoregressions (VAR) and show that consistent estimates of the loadings parameters can be obtained. In the first step, principal components are extracted from the data to formfactor estimates. In the second step, the parameters of the loadings VARs are estimated as a set of linear regression models with time-varying coefficients. We document the finite-sample properties of the maximum likelihood estimator through an extensive simulation study and illustrate the empirical relevance of the time-varying loadings structure using a large quarterly dataset for the US economy.

Publication Type:	Article
Additional Information:	© 2018 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	Factor models, Time-varying loadings, Consistent estimation, Maximum likelihood estimation, Two-step estimation, Principal components
Subjects:	E History America > E151 United States (General) H Social Sciences > HC Economic History and Conditions H Social Sciences > HG Finance
Departments:	Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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