Application of Stein Rules to Combination Forecasting
AbstractThe authors propose some Stein-rule combination forecasting methods that are designed to ameliorate the estimation of risk inherent in making operational the variance-covariance method for constructing combination weights. By Monte Carlo simulation, it is shown that this amelioration can be substantial in many cases. Moreover, generalized Stein-rule combinations are proposed that offer the user the opportunity to enhance combination forecasting performance when shrinking the feasible variance-covariance weights toward a fortuitous shrinkage point. In an empirical exercise, the proposed Stein-rule combinations performed well relative to competing combination methods.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 9 (1991)
Issue (Month): 4 (October)
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- Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, School of Economics and Management, University of Aarhus.
- Steven D. Levitt, 2003. "How Do Markets Function? An Empirical Analysis of Gambling on the National Football League," NBER Working Papers 9422, National Bureau of Economic Research, Inc.
- Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
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