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pro vyhledávání: '"Claeskens, Gerda"'
The composite quantile estimator is a robust and efficient alternative to the least-squares estimator in linear models. However, it is computationally demanding when the number of quantiles is large. We consider a model-averaged quantile estimator as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1131::f460badb0e9281fd86980789d617af0e
https://lirias.kuleuven.be/handle/123456789/627929
https://lirias.kuleuven.be/handle/123456789/627929
This paper offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1131::7fbc0bd548a7b9fe10efdc7c60ccf442
https://lirias.kuleuven.be/handle/123456789/532152
https://lirias.kuleuven.be/handle/123456789/532152