Forecasting in Dynamic Factor Models Using Bayesian Model Averaging

16 Pages Posted: 14 Dec 2004

See all articles by Gary Koop

Gary Koop

University of Leicester - Department of Economics

Simon Potter

Peter G. Peterson Institute for International Economics

Abstract

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.

Suggested Citation

Koop, Gary M. and Potter, Simon, Forecasting in Dynamic Factor Models Using Bayesian Model Averaging. Available at SSRN: https://ssrn.com/abstract=625768

Gary M. Koop (Contact Author)

University of Leicester - Department of Economics ( email )

University Road
Leicester LE1 7RH
United Kingdom

Simon Potter

Peter G. Peterson Institute for International Economics ( email )

1750 Massachusetts Avenue, NW
Washington, DC 20036
United States

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