Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging
This paper considers the problem of forecasting in large macroeconomic panels 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.
Year of publication: |
2003-01
|
---|---|
Authors: | Koop, Gary ; Potter, Simon |
Institutions: | Department of Economics, Leicester University |
Saved in:
freely available
Saved in favorites
Similar items by person
-
The Vector Floor and Ceiling Model
Koop, Gary, (2000)
-
Bayesian Semiparametric Inference in Multiple Equation Models
Koop, Gary, (2003)
-
Re-examining the Consumption-Wealth Relationship: The Role of Model Uncertainty
Koop, Gary, (2005)
- More ...