Estimation with Many Instruments

建议编号：0617836机构：麻省理工学院国家科学基金计划：经济学首席研究员：纽威，惠特尼标题：使用多种工具进行估计工具变量(IV)和广义矩方法(GMM)估计器在应用经济学中被广泛用于估计因果或结构性影响。这些估计者估计的精确度常常是一个令人担忧的问题。为了提高精度，经常使用许多仪器，这导致了偏差或较差的分布近似。因此，提高这些估计者的精确度将极大地改善经济学中的推断，从而改善应用计量经济学的进行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度量。建议的研究包括两个项目：(I)具有异方差的工具变量和(Ii)时间序列中的GMM。这项研究将结合刀切工具变量估计的正向和反向版本，开发一种具有多种工具的IV估计，它对异方差具有稳健性。研究还将开发基于偏差修正GMM目标函数的时间序列GMM估计器。这项研究的结果将在因果效应和结构效应的推断方面带来量子改进。估计这样的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经验工作和从这种经验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应用工作中是很常见的。这个项目将为工具变量估计者提供许多工具。此外，时间序列中也经常使用带有许多工具(由滞后形成)的GMM估计量。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，这些估计器被用来确定各种治疗的实验效果。这项拟议的研究可以改进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.