Econometric Methods to Study the Effects of Public Interventions and Terrorism

项目名称：NBERNSF PROPOSAL NO：0350645 INSTUTION：NBERNSF PROGRAM：ECONOMICSPI：Abadie，Alberto Title：研究公共干预和恐怖主义影响的计量经济学方法这项研究由两个项目组成，将开发创新的计量经济学方法来研究公共政策干预的效果。第一个项目研究用于治疗效果估计和缺失数据补偿的简单匹配方法。研究平均治疗效果的简单匹配估计值的分布的新的分析技术为解决两个突出问题开辟了道路：(A)估计治疗效果和其他治疗效果的最流行的匹配程序的性质-关于估计倾向分数的简单匹配估计值-还没有被很好地理解。这个项目将填补文献中的这一空白，将最近的发展扩展到对估计的协变量(如倾向分数)进行匹配的情况。(B)CPS和其他大型调查使用一种称为“热甲板”的配对程序来推算缺失的数值。治疗效果的简单匹配估计值中的大偏差表明，热甲板归罪程序将产生类似的偏差。这项研究将研究没有这些偏见的改进的热甲板程序的性质。对政策干预效果的研究通常是在总体水平上进行的，并使用没有受到利益干预的经济体作为对照组。这种方法有两个问题：(1)在如何选择控制组方面存在一些模糊，研究人员使用主观亲和力测量在可能的控制组之间进行选择。(2)如果使用汇总数据进行分析，通常的横截面标准误差不适用，这些标准误差仅衡量汇总变量的不确定性。这些模型中的不确定性是关于控制组在没有干预的情况下再现风险敞口经济体行为的能力。该项目将研究数据驱动程序的特性，以构建适当的控制组。这一程序限制了选择控制组的自由裁量权，迫使经验研究人员使用观察到的可量化特征来证明风险敞口和非风险敞口经济体之间的亲和力。此外，该项目还将研究如何利用时间序列信息来衡量对照对照组质量的不确定性。倾向得分匹配估计量的性质和基于热甲板数据的估计性质是经济学实证研究中的重要问题。此外，对这些性质的研究将导致计量经济学中新的分析方法的开发和应用。本研究开发的方法还将用于为STATA计量经济学软件包编写软件，以便更广泛地使用。该项目还将有助于公共政策的有效开发和实施。

ABSTRACTPROPOSAL NO: 0350645INSTITUTION: NBERNSF PROGRAM: ECONOMICSPI: Abadie, AlbertoTITLE: Econometric Methods to Study the Effects of Public Interventions and TerrorismThis research, consisting of two projects, will develop innovative econometric methods to study the effects of public policy interventions. The first project studies simple matching methods for treatment effects estimation and missing data imputation. New analytical techniques for the study of the distribution of simple matching estimators of average treatment effects have opened the way to solve two outstanding problems: (a) The properties of the most popular matching procedures to estimate the effects of and other treatment effects---simple matching estimators on the estimated propensity score---are not well understood. This project will fill this gap in the literature by extending recent development to the case when matching is done on estimated covariates (as the propensity score). (b) The CPS and other large surveys use a matching procedure known as 'hot deck' to impute missing values. Large biases in simple matching estimators of treatment effects indicate that hot deck imputation procedures will produce similar biases. This research will study the properties of an improved hot deck procedure that is free of these biases.Studies of the effects of policy interventions often take place at an aggregate level and use economies not exposed to the intervention of interest as a control group. This approach is subject to two problems: (i) there is some ambiguity about how to choose control groups with researchers using subjective measures of affinity to select between possible controls. (ii) If aggregate data are used for the analysis, the usual cross-section standard errors, which measure only uncertainty about aggregate variables, do not apply. Uncertainty in these models is about the ability of the control group to reproduce the behavior of the exposed economies in absence of the intervention. This project will study the properties of data-driven procedures to construct adequate control groups. This procedure limits the discretion in the choice of the control group, forcing the empirical researcher to demonstrate the affinities between the exposed and non-exposed economies using observed quantifiable characteristics. In addition, this project will study how to measure uncertainty about the quality of the comparison control groups using time series information. The properties of propensity score matching estimators and estimators based on data imputed by 'hot deck 'are important problems empirical research in economics. Moreover, the study of these properties will entail the development and application of new analytical methods in econometrics. The methods developed in this research will also be used to write software for the STATA econometric package for wider use. The project will also help in the efficient development and implementation of public policy.