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基于随机森林的分位数控制法AI 解读

This paper studies robust inference procedure for treatment effects in panel data with flexible relationship across units via the random forest method. The key contribution of this paper is twofold. First, we propose a direct construction of prediction intervals for the treatment effect by exploiting the information of the joint distribution of the cross-sectional units using random forest. In particular, we propose a Quantile Control Method (QCM) using the Quantile Random…

离散程度、偏度与峰度回归：美国工资结构应用AI 解读

ABSTRACT Quantile regression provides a powerful tool for investigating the effects of covariates on key quantiles of a conditional distribution. However, we often lack a general picture of how covariates affect the overall shape of the conditional distribution. Using quantile regression estimation and quantile‐based measures of spread, skewness, and kurtosis, we propose spread regression, skewness regression, and kurtosis regression as empirical tools to quantify the…

扩散模型波动率函数的渐近分布无关检验AI 解读

This paper develops two tests for parametric volatility function of a diffusion model based on Khmaladze (1981)’s martingale transformation. The tests impose no restrictions on the functional form of the drift function and are shown to be asymptotically distribution-free. The tests are consistent against a large class of fixed alternatives and have nontrivial power against a class of root-n local alternatives. The paper also extends the tests of volatility to testing for…