期刊名称：Discussion Papers / Business School, University of Strathclyde
出版年度：2009
卷号：2009
出版社：University of Strathclyde
摘要：Block factor methods offer an attractive approach to forecasting with many predictors. These extract
the information in these predictors into factors reflecting different blocks of variables (e.g. a price block,
a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks
as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows
for different parsimonious forecasting models to hold at different points in time. In this paper, we use
dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically
alter the weights attached to different forecasting models as evidence comes in about which has forecast
well in the recent past. In an empirical study involving forecasting output growth and inflation using
139 UK monthly time series variables, we find that the set of predictors changes substantially over time.
Furthermore, our results show that dynamic model averaging and model selection can greatly improve
forecast performance relative to traditional forecasting methods.
关键词：Bayesian, state space model, factor model, dynamic model averaging
