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A focused information criterion for quantile regression: Evidence for the rebound effect
A focused information criterion for quantile regression: Evidence for the rebound effect
In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for the purpose-specific choice of model specifications. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular focus parameter, but not for another. Using the FIC concept that is developed by BEHL, CLAESKENS and DETTE (2014) for quantile regression analysis, and the estimation of the rebound effect in individual mobility behavior as an example, this paper provides for an empirical application of the FIC in the selection of quantile regression models.
Discussion Paper / SFB823;39, 2016
- TU Dortmund University Germany
Dewey Decimal Classification: ddc:330
D2, info:eu-repo/classification/ddc/330, 330, fuel efficiency, C3, info:eu-repo/classification/ddc/310, information criteria, 310, info:eu-repo/classification/ddc/620, price elasticities, 620
D2, info:eu-repo/classification/ddc/330, 330, fuel efficiency, C3, info:eu-repo/classification/ddc/310, information criteria, 310, info:eu-repo/classification/ddc/620, price elasticities, 620
Dewey Decimal Classification: ddc:330
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!- TU Dortmund University Germany
In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for the purpose-specific choice of model specifications. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular focus parameter, but not for another. Using the FIC concept that is developed by BEHL, CLAESKENS and DETTE (2014) for quantile regression analysis, and the estimation of the rebound effect in individual mobility behavior as an example, this paper provides for an empirical application of the FIC in the selection of quantile regression models.
Discussion Paper / SFB823;39, 2016