Koop, Gary and Korobilis, Dimitris (2011) UK macroeconomic forecasting with many predictors : which models forecast best and when do they do so? Economic Modelling, 28 (5). pp. 2307-2318. ISSN 0264-9993Full text not available in this repository. (Request a copy from the Strathclyde author)
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.
|Keywords:||Bayesian, state space model, factor models, dynamic model averaging, Economic Theory, Economics and Econometrics|
|Subjects:||Social Sciences > Economic Theory|
|Department:||Strathclyde Business School > Economics|
|Depositing user:||Pure Administrator|
|Date Deposited:||02 Nov 2011 15:27|
|Last modified:||18 Nov 2016 03:32|