Please use this identifier to cite or link to this item:
Kim, Hyun Hak
Swanson, Norman R.
Year of Publication: 
Series/Report no.: 
Working Paper 2011-19
In this paper, we empirically assess the predictive accuracy of a large group of models based on the use of principle components and other shrinkage methods, including Bayesian model averaging and various bagging, boosting, LASSO and related methods Our results suggest that model averaging does not dominate other well designed prediction model specification methods, and that using a combination of factor and other shrinkage methods often yields superior predictions. For example, when using recursive estimation windows, which dominate other windowing approaches in our experiments, prediction models constructed using pure principal component type models combined with shrinkage methods yield mean square forecast error best models around 70% of the time, when used to predict 11 key macroeconomic indicators at various forecast horizons. Baseline linear models (which win around 5% of the time) and model averaging methods (which win around 25% of the time) fare substantially worse than our sophisticated nonlinear models. Ancillary findings based on our forecasting experiments underscore the advantages of using recursive estimation strategies, and provide new evidence of the usefulness of yield and yield-spread variables in nonlinear prediction specification.
Bayesian model averaging
ridge regression
least angle regression
elastic net and non-negative garotte
Document Type: 
Working Paper

Files in This Item:
477.53 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.