Instrumental Variable Estimation with Heteroskedasticity and Many Instruments
2010/08/10
研討會日期 : 2010-08-10
時間 : 14:30
主講人 : Professor John C. Chao
地點 : C103
演講者簡介 : John C. Chao got his Ph.D. in Economics from Yale University in 1994. He is currently serving as Associate Professor of Economics at University of Maryland. His research interest is in IV regressions with many instruments, Bayesian econometrics, and the use of model selection methods in nonstationary time series analysis.
演講摘要 : 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 likeWhite’s (1982), with additional terms that account for many instruments. They are consistent under standard, many instrument, and many weak instrument asymptotics. Based on a series of Monte Carlo experiments, we find that the estimators perform as well as LIML or Fuller (1977) under homoskedasticity, and have much lower bias and dispersion under heteroskedasticity, in nearly all cases considered.
