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Likelihood-based Analysis for Dynamic Factor Models

Borus Jungbacker () and Siem Jan Koopman ()
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Borus Jungbacker: VU University Amsterdam

No 08-007/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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. See also the publication in 'The Econometrics Journal' , 2015, 18(2), C1-C21.

Keywords: EM algorithm; Kalman Filter; Forecasting; Latent Factors; Markov chain Monte Carlo; Principal Components; State Space (search for similar items in EconPapers)
JEL-codes: C33 C43 (search for similar items in EconPapers)
Date: 2008-01-17, Revised 2014-03-20
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