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Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints.

Abstract:

A novel proposal for combining forecast distributions is to use quantile regression to combine quantile estimates. We consider the usefulness of the resultant linear combining weights. If the quantile estimates are unbiased, then there is strong intuitive appeal for omitting the constant and constraining the weights to sum to unity in the quantile regression. However, we show that suppressing the constant renders one of the main attractive features of quantile regression invalid. We establish...

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Publisher:
Taylor and Francis
Journal:
Journal of Applied Statistics More from this journal
Volume:
25
Issue:
2
Pages:
193 - 206
Publication date:
1998-01-01
ISSN:
0266-4763
Language:
English
UUID:
uuid:6b60c892-dbb9-495d-973f-e74b51fdb078
Local pid:
oai:economics.ouls.ox.ac.uk:14861
Deposit date:
2011-08-16

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