This note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, and show its connection to least squares models. The bias structure suggests that the instrumental variables estimator proposed for least squares can be applied to the...

Les régressions quantiles sont des outils statistiques dont l'objet est de décrire l'impact de variables explicatives sur une variable d'intérêt. Elles permettent une description plus riche que les régressions linéaires classiques, puisqu'elles s'intéressent à l'ensemble de la...

We analyze the impact of policing and socio-economic variables on crime in England and Wales during 1992-2007 using the quantile regression model which enables us to analyze different points of the crime distribution. The quantile regression model allows us to analyze whether or not the factors...

Previous literature has produced mixed results on the effects of country health expenditures on longevity. More importantly, all previous studies have evaluated the expenditure effects on the mean of the life expectancy distribution, ignoring the possibility that the expenditure returns may not...

We consider theoretical bootstrap coupling techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of coupling bootstrap techniques are developed for additive models with both symmetric error...

A daily systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilising tail event information. FRM (Financial Risk Meter) is based on Lasso quantile regression designed to capture tail event co-movements. The FRM focus lies on...

Cryptocurrencies are gaining momentum in investor attention, are about to become a new asset class, and may provide a hedging alternative against the risk of devaluation of fiat currencies following the COVID-19 crisis. In order to provide a thorough understanding of this new asset class, risk...

Let (X1, Y1), . . ., (Xn, Yn) be i.i.d. rvs and let l(x) be the unknown p-quantile regression curve of Y on X. A quantile-smoother ln(x) is a localised, nonlinear estimator of l(x). The strong uniform consistency rate is established under general conditions. In many applications it is necessary...

We consider theoretical bootstrap \coupling" techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of \coupling" bootstrap techniques are developed for additive models with both symmetric error...

We consider theoretical bootstrap "coupling" techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of "coupling" bootstrap techniques are developed for additive models with both symmetric error...