In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a … standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance … used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for …

In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a … standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance … used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for …

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 …

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 …

and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the …

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 …