In the presence of conditional heteroskedasticity, inference about the coefficients in a linear regression model these … days is typically based on the ordinary least squares estimator in conjunction with using heteroskedasticity consistent … standard errors. Similarly, even when the true form of heteroskedasticity is unknown, heteroskedasticity consistent standard …

Conditional heteroskedasticity can be exploited to identify the structural vector autoregressions (SVAR) but the … heteroskedastic SVAR-GARCH model and propose a bootstrap-based inference procedure on structural impulse responses. We compare the … finite-sample properties of our bootstrap method with those of two competing bootstrap methods via extensive Monte Carlo …

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 …

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 …

The aim of this paper is to illustrate more than one instance of poor bootstrap performance, and to see how available … important to explain bootstrap discrepancy are illustrated by some Monte Carlo experiments. …

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. …

. The GMM estimators outperform SML in the presence of misspecification in terms of multiplicative heteroskedasticity. This … holds in particular for the three-stage GMM estimator. Allowing for heteroskedasticity over time increases the robustness … with respect to misspecification in terms of ultiplicative heteroskedasticity. An application to the product innovation …

-statistics. Monte Carlo simulations are provided to deal with heteroskedasticity and to make some comparisons between the generalized …