Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints
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 constra...
| Main Authors: | , |
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| Format: | Journal article |
| Published: |
1998
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| _version_ | 1826273519570255872 |
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| author | Taylor, J Bunn, D |
| author_facet | Taylor, J Bunn, D |
| author_sort | Taylor, J |
| collection | OXFORD |
| description | 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 necessary and sufficient conditions for unbiasedness of a quantile estimate, and show that a combination with zero constant and weights that sum to unity is not necessarily unbiased. |
| first_indexed | 2024-03-06T22:29:25Z |
| format | Journal article |
| id | oxford-uuid:57cb387d-a9dc-4f00-86c1-21eab1895dac |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:29:25Z |
| publishDate | 1998 |
| record_format | dspace |
| spelling | oxford-uuid:57cb387d-a9dc-4f00-86c1-21eab1895dac2022-03-26T16:58:50ZCombining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing ConstraintsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:57cb387d-a9dc-4f00-86c1-21eab1895dacSaïd Business School - Eureka1998Taylor, JBunn, DA 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 necessary and sufficient conditions for unbiasedness of a quantile estimate, and show that a combination with zero constant and weights that sum to unity is not necessarily unbiased. |
| spellingShingle | Taylor, J Bunn, D Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints |
| title | Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints |
| title_full | Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints |
| title_fullStr | Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints |
| title_full_unstemmed | Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints |
| title_short | Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints |
| title_sort | combining forecast quantiles using quantile regression investigating the derived weights estimator bias and imposing constraints |
| work_keys_str_mv | AT taylorj combiningforecastquantilesusingquantileregressioninvestigatingthederivedweightsestimatorbiasandimposingconstraints AT bunnd combiningforecastquantilesusingquantileregressioninvestigatingthederivedweightsestimatorbiasandimposingconstraints |