Department of Economics and Business Economics

Likelihood-based dynamic factor analysis for measurement and forecasting

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We present new results for the likelihood-based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and for the parameter estimation by maximum likelihood methods. We also present the implications of our results for models with regression effects, for Bayesian analysis, for signal extraction, and for forecasting. An empirical illustration is provided for the analysis of a large panel of macroeconomic time series.

Original languageEnglish
JournalEconometrics Journal
Pages (from-to)C1-C21
Publication statusPublished - Jun 2015

    Research areas

  • EM algorithm, Kalman filter, Latent factors, Maximum likelihood, State space form

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