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概率统计学术报告

编辑:wfy 时间:2018年11月13日 访问次数:284


题目:Extreme Quantile Estimation for Autoregressive Models

时间:11月16日(周五)下午15:00

地点:工商楼105报告厅

报告人:黎德元教授


摘要:Quantile autoregresive model is a useful extension to classical autoregresive models as it can capture the influences of conditioning variables on the location, scale and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this paper, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time series data based on extreme value theory. We build the connection between the second-order conditions for the autoregression coefficients and for the conditional quantile functions, and establish the asymptotic properties of the proposed estimator. The finite sample performance of the proposed method is illustrated through a simulation study and the analysis of US retail gasoline price.

 

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联系人:张荣茂(rmzhang@zju.edu.cn)