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利用特殊回归变量识别第一类与第二类错误概率：司法误判应用AI 解读

We nonparametrically identify Type I and Type II error rates when mistakes are unobserved. Our strategy reframes the setting as a misclassified binary choice model, using a special regressor to identify the misclassification probabilities. Identification requires an exclusion restriction and a large support condition. When the exclusion restriction is in doubt, we show how our estimands can be interpreted as bounds under a weaker monotonicity condition. Bounds are also…

利用双重机器学习估计直接与间接分位数处理效应AI 解读

We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of…

生命周期偏差调整与犯罪的代际关联AI 解读

Using age-specific measures as proxies for lifetime outcomes can introduce bias into estimates of intergenerational associations. We extend the generalized errors-in-variables (GEiV) model to the estimation of intergenerational crime associations and implement a data-driven method to correct for life-cycle bias. The GEiV adjustment accounts for both the extensive and intensive margins and introduces an elasticity estimator that accommodates zeros. Our results show that…

具有动态条件依赖的图形 Copula GARCH 模型AI 解读

Modeling returns on large portfolios is a challenging problem as the number of parameters in the covariance matrix grows as the square of the size of the portfolio. Traditional correlation models, for example, the dynamic conditional correlation (DCC)-GARCH model, often ignore the nonlinear dependencies in the tail of the return distribution. In this paper, we aim to develop a framework to model the nonlinear dependencies dynamically, namely the graphical copula GARCH…

Bootstrap 模型平均AI 解读

Model averaging has garnered significant attention in recent years for its ability to combine information from multiple models. A critical challenge in frequentist model averaging is determining the appropriate weight vector. The bootstrap method, well-known for its desirable properties, offers a promising solution. In this article, we propose a bootstrap model averaging approach that selects weights by minimizing a bootstrap-based criterion. Notably, our weight selection…

条件平均处理效应的估计AI 解读

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE parameter is nonparametrically…

不确定数据的信号提取：状态空间方法AI 解读

Most macroeconomic data are uncertain—they are estimates rather than perfect measures of underlying economic variables. One symptom of that uncertainty is the propensity of statistical agencies to revise their estimates in the light of new information or methodological advances. This paper sets out an approach for extracting the signal from uncertain data. It describes a two-step estimation procedure in which the history of past revisions is first used to estimate the…