Estimation with Many Instruments

使用多种仪器进行估算

• 批准号: 0617836
• 负责人: Whitney Newey
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0617836&HistoricalAwards=false
• 关键词: Estimation; Many; Instruments

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.

提案编号：0617836机构：马萨诸塞州理工学院NSF项目：经济学主要研究者：纽维，惠特尼标题：估计与许多工具摘要工具变量（IV）和广义矩法（GMM）估计被广泛用于应用经济学估计因果或结构效应。这些估计量的估计精度经常是一个问题。经常使用许多工具来提高精度，导致偏差或差的分布近似。因此，提高这些估计的精度将大大改善经济学的推论，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。 拟议的研究将开发更好的估计和更好的措施，IV和GMM估计精度。该研究包括两个项目：（i）异方差工具变量和（ii）时间序列中的GMM。 本研究将结合联合收割机的前向和反向版本的刀切工具变量估计和开发一个IV估计，与许多工具，这是强大的异方差。 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和结构效应的推论的量子改进。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中的实证工作以及从这些实证工作中得出的政策结论产生广泛的影响。例如，异方差一致标准误差的使用在应用工作中非常常见。这个项目将提供这样的工具变量估计与许多工具。此外，具有许多工具（由滞后形成）的GMM估计器经常用于时间序列。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其他学科，包括生物统计学和政治学中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退出治疗时，这些估计量用于确定各种治疗的实验效果。建议的研究可以改善因果推理在这一非常重要的工作。