Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence

Hyun Hak Kim and Norman Swanson ()

Departmental Working Papers from Rutgers University, Department of Economics

Abstract: 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 winaround 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.

Keywords: prediction, bagging; boosting, Bayesian model averaging,, ridge regression; least angle regression, elastic net and non-negative garotte (search for similar items in EconPapers)
JEL-codes: G1 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2011-05-15
New Economics Papers: this item is included in nep-cba, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Journal Article: Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201119

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