heteroskedasticity of unknown form is introduced. …

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literatures. Although … that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap … conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type or …

with Generalised AutoRegressive Conditional Heteroskedasticity are characterised by the ability to estimate and forecast …

We examine several modified versions of the heteroskedasticity-consistent covariance matrix estimator of Hinkley and … several tests for heteroskedasticity and find that it may be wise to employ the jackknife heteroskedasticity …-consistent covariance matrix even in the absence of detected heteroskedasticity. …

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. …

with Generalised AutoRegressive Conditional Heteroskedasticity are characterised by the ability to estimate and forecast …

We present a new genuine jackknife estimator for instrumental variable inference with unknown heteroskedasticity. It …

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. …

We derive tests for heteroskedasticity after fixed effects estimation of linear panel models. The asymptotic results …

with fixed regressors. These tests are exact, distribution-free, robust against heteroskedasticity of unknown form, and … supposed to be robust against heteroskedasticity. The results show that our procedures are superior. …