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Selecting the Form of Combining Regressions Based on Recur sive Prediction Criteria

  • Liang Kou-yuan
  • Ryu Keunkwan

Abstract

This paper reformulates the basic Granger and Ramanathan’s (1984) combining regression framework based on post-sample predictive accuracies. Using recursive regression techniques, this paper develops an algorithm to estimate combining weights. Under the new prediction criteria, we show that Granger and Ramanathan’s (1984) preference ordering, Method C → Method A → Method B, breaks down. To overcome this lack of ordering, the paper suggests that, by using Akaike’s (1973) information or Amemiya’s (1980) prediction criterion, one can recursively select the best form of combining regressions. Empirical examples using macroeconomic forecasts of Taiwan are presented to illustrate the validity of the theoretical arguments.

Keywords

Mean Square Error Prediction Error ARIMA Model Government Purchase Error Reduction Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Liang Kou-yuan
    • 1
  • Ryu Keunkwan
    • 2
  1. 1.Department of EconomicsNational Tsing Hua UniversityHsin-chuTaiwan
  2. 2.Department of EconomicsUniversity of CaliforniaLos AngelesUSA

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