Report

Forecasting in large macroeconomic panels using Bayesian Model Averaging

Abstract: This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible models. We explain 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. Our analysis indicates that models containing factors do outperform autoregressive models in forecasting both GDP and inflation, but only narrowly and at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of the dependent variable seem to contain most of the information relevant for forecasting.

Keywords: Bayesian; forecasting; panel;

JEL Classification: C11; C53; E37;

Bibliographic Information

Provider: Federal Reserve Bank of New York

Part of Series: Staff Reports

Publication Date: 2003-03-01

Number: 163

Note: For a published version of this report, see Gary Koop and Simon Potter, "Forecasting in Dynamic Factor Models Using Bayesian Model Averaging," Econometrics Journal 7, no. 2 (2004): 550-65.