Kernel Quantile Regression for Nonlinear Stochastic Models
نویسندگان
چکیده
We consider kernel quantile estimates for drift and scale functions in nonlinear stochastic regression models. Under a general dependence setting, we establish asymptotic point-wise and uniform Bahadur representations for the kernel quantile estimates. Based on those asymptotic representations, central limit theorems are obtained. Applications to nonlinear autoregressive models and linear processes are made. Simulation studies show that the estimates have good performance. The results are applied to the Pound/USD exchange rates data.
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