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Likelihood-based Analysis for Dynamic Factor Models

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Author Info

  • Borus Jungbacker

    ()
    (VU University Amsterdam)

  • Siem Jan Koopman

    ()
    (VU University Amsterdam)

Abstract

We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and parameter estimation by maximum likelihood and Bayesian methods. An illustration is provided for the analysis of a large panel of macroeconomic time series.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-007/4.

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Date of creation: 17 Jan 2008
Date of revision: 20 Mar 2014
Handle: RePEc:dgr:uvatin:20080007

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Web page: http://www.tinbergen.nl

Related research

Keywords: EM algorithm; Kalman Filter; Forecasting; Latent Factors; Markov chain Monte Carlo; Principal Components; State Space;

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