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 …) under homoskedasticity, and has much lower bias and dispersion under heteroskedasticity, in nearly all cases considered. …

inference methods that allow for many covariates and heteroskedasticity. Our results are obtained using high … versions of Eicker-White heteroskedasticity consistent standard error estimators for linear models are inconsistent under this … asymptotics. We then propose a new heteroskedasticity consistent standard error formula that is fully automatic and robust to both …

covariates and heteroskedasticity. Our results are obtained using high-dimensional approximations, where the number of covariates … are allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker-White heteroskedasticity … heteroskedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroskedasticity of …

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 … homoskedasticity, and have much lower bias and dispersion under heteroskedasticity, in nearly all cases considered. …

This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It … when the number instruments is fixed and there is homoskedasticity. This test improves on recently proposed tests by … allowing for heteroskedasticity and by avoiding assumptions on the instrument projection matrix. The asymptotics is based on …

accompanying consistent standard errors in the presence of heteroskedasticity and many instruments. The asymptotic framework … inconsistent in the presence of heteroskedasticity, unless Kn/rn → 0. We also show that the rate of convergence and the form of the …

tests by allowing for heteroskedasticity and by avoiding assumptions on the instrument projection matrix. This paper finds …This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It …–Sargan or GMM tests of overidentifying restrictions. …

covariates and heteroskedasticity. Our results are obtained using high-dimensional approximations, where the number of covariates … are allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker-White heteroskedasticity … heteroskedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroskedasticity of …

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 … homoskedasticity, and have much lower bias and dispersion under heteroskedasticity, in nearly all cases considered. …

This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It … when the number instruments is fixed and there is homoskedasticity. This test improves on recently proposed tests by … allowing for heteroskedasticity and by avoiding assumptions on the instrument projection matrix. The asymptotics is based on …