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Title: Functional-coefficient quantile regression for panel data with latent group structure Authors:  Degui Li - University of York (United Kingdom) [presenting]
Abstract: Estimating functional-coefficient models is considered in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group structure and number are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodology and theory are applied to identify the latent group structure in linear panel quantile regression (uniformly over quantile levels) and model individual effects.