Instrumental variable estimation with heteroskedasticity and many instruments

• 成果类型: Article
• 署名作者: Hausman, Jerry A.; Newey, Whitney K.; Woutersen, Tiemen; Chao, John C.; Swanson, Norman R.
• 署名单位: Massachusetts Institute of Technology (MIT); University of Arizona; University System of Maryland; University of Maryland College Park; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE89
• 发表日期: 2012
• 页码: 211-255
• 关键词: instrumental variables; heteroskedasticity; many instruments; jackknife

摘要

This paper gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data. Such settings are common in microeconometric applications where many instruments are used to improve efficiency and allowance for heteroskedasticity is generally important. The solution is a Fuller (1977) like estimator and standard errors that are robust to heteroskedasticity and many instruments. We show that the estimator has finite moments and high asymptotic efficiency in a range of cases. The standard errors are easy to compute, being like White's (1982), with additional terms that account for many instruments. They are consistent under standard, many instrument, and many weak instrument asymptotics. We find that the estimator is asymptotically as efficient as the limited-information maximum likelihood (LIML) estimator under many weak instruments. In Monte Carlo experiments, we find that the estimator performs as well as LIML or Fuller (1977) under homoskedasticity, and has much lower bias and dispersion under heteroskedasticity, in nearly all cases considered.