Quantile autoregression. Commentary
Quantile autoregression. Commentary
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DOI：
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发表时间：
2006
影响因子：
3.7
通讯作者：
R. Koenker;Zhijie Xiao;Jianqing Fan;Yingying Fan;K. Knight;M. Hallin;B. Werker;C. Hafner;
中科院分区：
文献类型：
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作者：
R. Koenker;Zhijie Xiao;Jianqing Fan;Yingying Fan;K. Knight;M. Hallin;B. Werker;C. Hafner;
我们考虑分位数自回归（QAR）模型，其中自回归系数可以表示为单个标量随机变量的单调函数。这些模型可以捕捉条件变量对响应条件分布的位置、规模和形状的系统影响，从而构成经典常系数线性时间序列模型的重要扩展，在经典常系数线性时间序列模型中，条件作用仅限于位置移动。该模型可以解释为具有强相关系数的一般随机系数自回归模型的一个特例。研究了该模型和相关估计量的统计性质。导出了自回归分位数过程的极限分布。研究了QAR推理方法。该模型在美国失业率、短期利率和汽油价格上的实证应用凸显了该模型的潜力。
We consider quantile autoregression (QAR) models in which the autoregressive coefficients can be expressed as monotone functions of a single, scalar random variable. The models can capture systematic influences of conditioning variables on the location, scale, and shape of the conditional distribution of the response, and thus constitute a significant extension of classical constant coefficient linear time series models in which the effect of conditioning is confined to a location shift. The models may be interpreted as a special case of the general random-coefficient autoregression model with strongly dependent coefficients. Statistical properties of the proposed model and associated estimators are studied. The limiting distributions of the autoregression quantile process are derived. QAR inference methods are also investigated. Empirical applications of the model to the U.S. unemployment rate, short-term interest rate, and gasoline prices highlight the model's potential.