Econ Papers Daily / 工作论文
用于函数形式敏感性分析的 LLM 工作流
An LLM Workflow for Functional Form Sensitivity Analysis
摘要
We introduce an LLM workflow for functional form sensitivity analysis. A researcher estimates the value of an object of interest by maximizing the fit of a working model to some data. The working model assumes an arbitrary form for some function. We define sensitivity by how much it is possible to change the estimate of the object of interest, without compromising fit, by selecting a different functional form. The workflow first looks for an analytic solution. If it fails to find one, it approximates the solution via numerical methods that respect the fit constraint at each step of a solver path. The workflow yields a verifiable lower bound on the in-sample sensitivity of the researcher’s estimate, and is fast in many practical situations. We illustrate with an application to the welfare cost of adverse selection in health insurance.
中文摘要
我们引入一个用于函数形式敏感性分析的 LLM 工作流。研究者通过最大化某个工作模型对某些数据的拟合程度来估计某个目标对象的值。该工作模型对某个函数假定任意形式。我们将敏感性定义为：在不损害拟合程度的情况下，通过选择不同的函数形式，目标对象的估计值可能改变多少。该工作流首先寻找解析解。如果找不到解析解，则通过数值方法近似求解，并在求解器路径的每一步都遵守拟合约束。该工作流给出研究者估计值的样本内敏感性的可验证下界，并且在许多实际情形中速度很快。我们以健康保险中逆向选择的福利成本应用为例进行说明。