Standard Errors for Regression-Based Causal Effect Estimates in Economics Using Numerical Derivatives

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-03-25 DOI:10.1007/s10614-024-10565-w
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Abstract

The aim of nearly all empirical studies in economics is to provide scientific evidence that can be used to assess policy relevant cause-and-effect. In the context of the general potential outcomes framework, we review how a causal effect parameter can be rigorously but tractably specified, identified and estimated along with its asymptotic standard error. For cases in which the analytic and computational requirements for calculation of the ASE are challenging, we suggest the use of numerical derivatives (ND). We detail the specific type of ND software required for this purpose, and note that it is offered as a feature in most statistical packages. As an illustration, we analyze the causal effect of wife's high school graduation on family size using the Stata/Mata deriv command. Code for this example is supplied in an appendix.

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使用数值导数计算经济学中基于回归的因果效应估计的标准误差
摘要 几乎所有经济学实证研究的目的都是提供可用于评估政策相关因果关系的科学证据。在一般潜在结果框架的背景下,我们回顾了如何严格而简便地指定、确定和估计因果效应参数及其渐近标准误差。如果计算 ASE 的分析和计算要求具有挑战性,我们建议使用数值导数(ND)。我们将详细介绍为此目的所需的 ND 软件的具体类型,并指出大多数统计软件包都提供该功能。作为示例,我们使用 Stata/Mata deriv 命令分析了妻子高中毕业对家庭规模的因果效应。本例的代码见附录。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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