Template-Type: ReDIF-Paper 1.0 Series: Tinbergen Institute Discussion Papers Creation-Date: 2008-01-17 Revision-Date: 2014-03-20 Number: 08-007/4 Author-Name: Borus Jungbacker Author-Email: firstname.lastname@example.org Author-Workplace-Name: VU University Amsterdam Author-Name: Siem Jan Koopman Author-Email: email@example.com Author-Workplace-Name: VU University Amsterdam Title: Likelihood-based Analysis for Dynamic Factor Models 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. Classification-JEL: C33; C43 Keywords: EM algorithm; Kalman Filter; Forecasting; Latent Factors; Markov chain Monte Carlo; Principal Components; State Space File-Url: http://papers.tinbergen.nl/08007.pdf File-Format: application/pdf File-Size: 292317 bytes Handle: RePEc:tin:wpaper:20080007