Estimation with Many Instruments

提案编号：0617836 机构：麻省理工学院 NSF 项目：经济学 首席研究员：Newey, Whitney 标题：使用多种工具进行估计摘要工具变量 (IV) 和广义矩量法 (GMM) 估计器在应用经济学中广泛用于估计因果或结构效应。这些估算器的估算精度经常引起人们的关注。通常会使用许多仪器来提高精度，从而导致偏差或较差的分布近似值。因此，提高这些估计量的精度将极大地改善经济学的推理，从而改善应用计量经济学的实施以及从此类研究中得出的政策结论。 拟议的研究将为 IV 和 GMM 估计器开发更好的估计器和更好的精度测量。拟议的研究包括两个项目：（i）具有异方差性的工具变量和（ii）时间序列中的 GMM。 这项研究将结合正向和反向版本的折刀工具变量估计器，并开发一种具有多种工具的 IV 估计器，该估计器对异方差性具有鲁棒性。 该研究还将开发新的时间序列 GMM 估计器，该估计器基于偏差校正 GMM 目标函数。 这项研究的结果将导致因果效应和结构效应推论的量子改进。估计此类影响是经济实证工作最常见的目标。因此，这项工作应该对经济学的实证工作以及从此类实证工作得出的政策结论产生广泛的影响。例如，异方差一致性标准误的使用在应用工作中非常常见。该项目将为具有多种工具的工具变量估计器提供此类工具。此外，具有许多工具（由滞后形成）的 GMM 估计器也经常用于时间序列。这项工作将为这些应用提供更准确的方法。 拟议活动的更广泛影响将是其对其他学科（包括生物统计学和政治学）工具变量估计的影响。在生物统计学中，当受试者可以自行选择退出治疗时，这些估计量用于确定各种治疗的实验效果。拟议的研究可以改善这项非常重要的工作中的因果推断。

Proposal No: 0617836 Institution: Massachusetts Institute of Technology NSF Program: ECONOMICS Principal Investigator: Newey, Whitney Title: Estimation with Many InstrumentsABSTRACTInstrumental variables (IV) and Generalized Method of Moments (GMM) estimators are widely used in applied economics to estimate causal or structural effects. The precision of estimates from these estimators is frequently a concern. Often many instruments are used in an effort to improve precision, leading to bias or poor distributional approximations. Therefore improving the precision of these estimators will vastly improve inference in economics and therefore improve the conduct of applied econometrics and the policy conclusions that derive from such studies. The proposed research will develop better estimators and better measures of precision for IV and GMM estimators. The proposed research consists of two projects: (i) instrumental variables with heteroskedasticity and (ii) GMM in time series. This research will combine forward and reverse versions of jackknife instrumental variable estimators and develop an IV estimator, with many instruments, that is robust to heteroskedasticity. The research will also develop new time series GMM estimator that is based on bias correcting the GMM objective function. The result of this research will lead to quantum improvements in inferences of causal and structural effects. Estimating such effects is the most common goal of economic empirical work. Consequently this work should have a wide impact on empirical work in economics and the policy conclusions that are derived from such empirical work. For example, the use of heteroskedasticity consistent standard errors is very common in applied work. This project will provide such for instrumental variable estimators with many instruments. Also GMM estimators with many instruments (formed from lags) are often used in time series. This work will provide more accurate methods for these applications. The wider impact of the proposed activity would be its effect on instrumental variables estimation in other disciplines, including biostatistics and political science. In biostatistics these estimators are used to determine experimental effects of treatments of various kinds when subjects can self select out of treatment. The proposed research could improve causal inferences in this very important work.