Estimation with Many Instruments

机构：麻省理工学院国家科学基金项目：经济学首席研究员：Newey， Whitney标题：多种工具估计摘要工具变量（IV）和广义矩量法（GMM）估计量在应用经济学中广泛用于估计因果或结构效应。这些估计器的估计精度经常是一个问题。为了提高精度，经常使用许多工具，导致偏差或差的分布近似。因此，提高这些估计器的精度将大大改善经济学的推断，从而改善应用计量经济学的行为和从这些研究中得出的政策结论。提出的研究将为IV和GMM估计器开发更好的估计器和更好的精度度量。本研究包括两个项目：(i)异方差工具变量和（ii）时间序列的GMM。本研究将结合叠刀工具变量估计器的正向和反向版本，并开发一种IV估计器，与许多工具，对异方差具有鲁棒性。研究还将开发新的基于偏差校正GMM目标函数的时间序列GMM估计器。这项研究的结果将导致因果和结构效应推理的量子改进。估计这种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和从这些实证工作中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差的使用在实际工作中是非常常见的。这个项目将提供这样的工具变量估计与许多仪器。此外，使用许多工具（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它对包括生物统计学和政治学在内的其他学科的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不进行治疗时，这些估计量用于确定各种治疗的实验效果。提出的研究可以改善这一非常重要的工作中的因果推论。

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.