ESTIMATION OF NON-CROSSING QUANTILE REGRESSION CURVES

Quantile regression methods have been widely used in many research areas in recent years. However conventional estimation methods for quantile regression models do not guarantee that the estimated quantile curves will be non-crossing. While there are various methods in the literature to deal with this problem, many of these methods force the model parameters to lie within a subset of the parameter space in order for the required monotonicity to be satisfied. Note that different methods may use different subspaces of the space of model parameters. This paper establishes a relationship between the monotonicity of the estimated conditional quantiles and the comonotonicity of the model parameters. We develope a novel quasi-Bayesian method for parameter estimation which can be used to deal with both time series and independent statistical data. Simulation studies and an application to real financial returns show that the proposed method has the potential to be very useful in practice.