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处理效应模型中分布函数与分位数函数的估计和推断AI 解读

We propose inverse probability weighted estimators for the distribution functions of the potential outcomes under the unconfoundedness assumption and apply the inverse mapping to obtain the quantile functions. We show that these estimators converge weakly to zero mean Gaussian processes. A simulation method is proposed to approximate these limiting processes. Based on these results, we construct tests for stochastic dominance relations between the potential outcomes.…

使用条件自回归期望分位数模型评估风险价值AI 解读

In this paper we propose a downside risk measure, the expectile-based Value at Risk (EVaR), which is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). The index [theta] of an EVaR is the relative cost of the expected margin shortfall and hence reflects the level of prudentiality. It is also shown that a given expectile corresponds to the quantiles with distinct tail probabilities under different distributions. Thus, an EVaR…