Maximum Likelihood Estimation of Time-Varying Loadings in High-Dimensional Factor Models

Jakob Mikkelsen (), Eric Hillebrand () and Giovanni Urga
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Eric Hillebrand: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: In this paper, we develop a 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 by a two-step maximum likelihood estimation procedure. In the first step, principal components are extracted from the data to form factor estimates. In the second step, the parameters of the loadings VARs are estimated as a set of univariate 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.

Keywords: High-dimensional factor models; dynamic factor loadings; maximum likelihood; principal components JEL classification: C33; C55; C13 (search for similar items in EconPapers)
Pages: 36
Date: 2015
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2015-61

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