Forecasting in dynamic factor models using Bayesian model averaging

G.M. Koop, S. Potter

Research output: Contribution to journalArticlepeer-review

18 Downloads (Pure)


This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. For both GDP and inflation, we find that the models which contain factors do out-forecast an AR(p), but only by a relatively small amount and only at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of dependent variable seem to contain most of the information relevant for forecasting. Relative to the small forecasting gains provided by including factors, the gains provided by using Bayesian model averaging over forecasting methods based on a single model are appreciable.
Original languageEnglish
Pages (from-to)550-565
Number of pages15
JournalEconometrics Journal
Issue number2
Publication statusPublished - Dec 2004


  • Bayesian model averaging
  • diffusion index
  • Markov chain
  • Monte Carlo model
  • composition
  • feference prior
  • econometrics


Dive into the research topics of 'Forecasting in dynamic factor models using Bayesian model averaging'. Together they form a unique fingerprint.

Cite this