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Copula-based nonlinear quantile autoregression

机译:基于Copula的非线性分位数自回归

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摘要

Parametric copulas are shown to be attractive devices for specifying quantile autoregressive models for nonlinear time-series. Estimation of local, quantile-specific copula-based time series models offers some salient advantages over classical global parametric approaches. Consistency and asymptotic normality of the proposed quantile estimators are established under mild conditions, allowing for global misspecification of parametric copulas and marginals, and without assuming any mixing rate condition. These results lead to a general framework for inference and model specification testing of extreme conditional value-at-risk for financial time series data.
机译:显示出参数关联是吸引人的设备,用于指定非线性时间序列的分位数自回归模型。与经典的全局参数方法相比,估计局部的,基于分位数的基于copula的时间序列模型具有一些明显的优势。建议的分位数估计量的一致性和渐近正态性是在温和条件下建立的,允许参数系数和边际的全局错误指定,并且不假设任何混合速率条件。这些结果为金融时间序列数据的极端条件风险价值的推理和模型规范测试提供了一个通用框架。

著录项

  • 来源
    《The econometrics journal》 |2009年第s1期|50-67|共18页
  • 作者单位

    Cowles Foundation for Research in Economics, Yale University, Box 208281, New Haven,CT 06520, USA;

    Department of Economics, University of Illinois at Urbana-Champaign, Champaign,IL 61820, USA;

    Department of Economics, Boston College, Chestnut Hill, MA 02467, USA and Tsinghua University, Beijing, 100084, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    copula; ergodic nonlinear markov models; quantile autoregression;

    机译:系词;遍历非线性马尔可夫模型;分位数自回归;

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