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Application of Stein Rules to Combination Forecasting

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  • Fomby, Thomas B
  • Samanta, Subarna K

Abstract

The 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 Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 9 (1991)
Issue (Month): 4 (October)
Pages: 391-407

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Handle: RePEc:bes:jnlbes:v:9:y:1991:i:4:p:391-407

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Cited by:
  1. Chan, Chi Kin & Kingsman, Brian G. & Wong, H., 1999. "The value of combining forecasts in inventory management - a case study in banking," European Journal of Operational Research, Elsevier, vol. 117(2), pages 199-210, September.
  2. 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.
  3. 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.
  4. 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.

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