Eric Hillebrand

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

Publikation: Working paperForskning

Dokumenter

  • rp15_61

    Forlagets udgivne version, 668 KB, PDF-dokument

  • Jakob Guldbæk Mikkelsen, Danmark
  • Eric Hillebrand
  • Giovanni Urga, Cass Business School, Storbritannien
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.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider36
StatusUdgivet - 17 dec. 2015
SerietitelCREATES Research Papers
Nummer2015-61

    Forskningsområder

  • High-dimensional factor models, dynamic factor loadings, maximum likelihood, principal components

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