Identification via heteroskedasticity exploits differences in variances across regimes to identify parameters in … parameter vector. I apply these tools to monetary policy shocks, identified using heteroskedasticity in high frequency data. I …

estimation, that incorporates heteroskedasticity in a natural way and does not use a generalized inverse. A Monte Carlo study … examines the performance of the statistic for different heteroskedasticity-robust variance estimators and different skedastic … empirical power, how one corrects for heteroskedasticity matters. We also compare its performance with that of the Wald …

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. …

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 …

heteroskedasticity-robust standard errors. This paper develops the "fixed-bandwidth" alternative asymptotic theory for RD designs, which … there is local heteroskedasticity. Feasible estimators of fixed-bandwidth standard errors are easy to implement and are akin … to treating RD estimators as locally parametric, validating the common empirical practice of using heteroskedasticity …