Title(题名):  Nonparametric Inference for Time-Varying Coefficient Quantile Regression
Authors(作者):  Weichi Wu and Zhou Zhou
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
Pages(页码):  p98-109
ISSN:  0735-0015
Abstract(摘要):  The article considers nonparametric inference for quantile regression models with time-varying coefficients. The errors and covariates of the regression are assumed to belong to a general class of locally stationary processes and are a]lowed to be cross-dependent. Simultaneous confidence tubes (SCTs) and integrated squared difference tests (ISDTs) are proposed for simultaneous nonparametric inference of the latter models with asymptotically correct coverage probabilities and Type I error rates. Our methodologies are shown to possess certain asymptotically optimal properties. Furthermore. We propose an information criterion that performs consistent model selection for nonparametric quantile regression models of non-stationary time series. For implementation, a wild bootstrap procedure is proposed, which is shown to be robust to the dependent and nonstationary data structure. Our method is applied to studying the asymmetric and time-varying dynamic structures of the U.S. unemployment rate since the l940s. Supplementary materials for this article are available online.
Key words(关键词):  Integrated squared difference test; Local linear quantile estimators: Local stationarity, Simultaneous confidence tubes; Time-varying models; Variable selection.
Where(馆藏地):  304外文报刊阅览室
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