tests to accommodate hidden dependence and non-stationarities involving heteroskedasticity, thereby uncoupling these tests … heteroskedasticity in the series. Related extensions are provided for testing cross-correlation at various lags in bivariate time series …

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 …

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. …

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 …

detect, in a manner that is robust to heteroskedasticity of possibly unknown form. This paper first discusses how to test for … these tests robust to heteroskedasticity of unknown form and discusses several related procedures for doing so. Finally, it …

detect, in a manner that is robust to heteroskedasticity of possibly unknown form. This paper first discusses how to test for … these tests robust to heteroskedasticity of unknown form and discusses several related procedures for doing so. Finally, it …

robust under cross sectional or time heteroskedasticity and inhomogeneous patterns of serial correlation. A Monte Carlo study …